A multi-layer transaction method and device of a virtual power plant and a storage medium

Abstract

The application discloses a kind of virtual power plant multilayer transaction method, device and storage medium, method includes: in day, the day market declaration situation of entity distribution network side is acquired, market clearing is carried out in virtual distribution network side and adjustable resource information is optimized configuration, virtual transmission network side according to the first optimization configuration result is declared and clearing, adjustment and output day generation and use plan;In day, the market demand of entity transmission network side is acquired, virtual distribution network side according to market demand carries out market clearing and adjustable resource information is optimized configuration, virtual transmission network side according to the second optimization configuration result is declared and clearing, and according to the second clearing result, market demand is iteratively optimized, obtains and outputs day generation and use plan, controls the market principal part of virtual distribution network side and executes the day generation and use plan of described.It improves the aggregation and collaborative optimization capability of resource, reduces the influence of prediction error on market clearing result and resource configuration optimization.

Description

Technical Field

[0001] This invention relates to the field of integrated energy technology, and in particular to a multi-level trading method, apparatus and storage medium for a virtual power plant. Background Technology

[0002] With the continuous maturation and development of new technologies such as electric vehicles and distributed resources, their large-scale integration into distribution networks enhances grid flexibility and enables the friendly consumption of renewable energy. However, due to the limitations of their connection points, they cannot directly participate in electricity market transactions at the transmission network level. Meanwhile, some research has proposed using virtual power plants to participate in market transactions and leverage the value of distributed resources. However, due to the limitations of virtual power plant technology, the participation model is relatively singular. Therefore, it is necessary to analyze market organization methods that are more widely applicable to the distribution network level, fully utilize the flexibility value of virtual distribution network resources, and achieve friendly interaction with the main grid.

[0003] Current research on the participation of virtual distribution network resources in the electricity market largely focuses on virtual power plant (VFP) solutions. A VFP is a coordinated management system that uses information technology and software systems to aggregate and collaboratively optimize various distributed resources, allowing it to function as a special type of power plant participating in the electricity market and grid operation. However, current resource aggregation and collaborative optimization technologies are still immature, and there are numerous difficulties in their organizational structure, resulting in poor adaptability of VFP solutions. Summary of the Invention

[0004] This invention provides a multi-level trading method, apparatus, and storage medium for virtual power plants to address the technical problems of poor resource aggregation and collaborative optimization capabilities, as well as the prediction errors caused by the volatility and randomness of new energy sources in market clearing results and resource allocation optimization.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a multi-tiered trading method for a virtual power plant, wherein the virtual power plant includes a virtual distribution network side and a virtual transmission network side, and the multi-tiered trading method includes:

[0006] During the day-ahead planning phase, the day-ahead market declaration information of distributed energy entities on the physical distribution network side is obtained. This information is then input into the first distribution network-side market optimization allocation model, enabling the virtual distribution network side to perform market clearing and optimize the allocation of adjustable resource information based on the day-ahead market declaration information, thereby obtaining a first optimized allocation result. The first optimized allocation result is then sent to the first transmission network-side market clearing model, allowing the virtual transmission network side to submit declarations and clear, obtain a first clearing result, and adjust the day-ahead generation and consumption plan based on the first clearing result, outputting the adjusted generation and consumption plan.

[0007] During intraday market trading, market demand on the physical transmission network side is acquired and input into the second distribution network side market optimization configuration model. This enables the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information, obtaining a second optimization configuration result. The second optimization configuration result is then sent to the second transmission network side market clearing model, allowing the virtual transmission network side to submit applications and clear its market, obtaining a second clearing result. Based on the second clearing result, market demand is iteratively optimized to obtain and output an intraday consumption plan, controlling the market participants on the virtual distribution network side to execute the intraday consumption plan.

[0008] As a preferred option, this application constructs a virtual distribution network side market optimization allocation model during the day-ahead and intraday market filing process, optimizes virtual distribution network side resources, fully leverages the adjustment capability of virtual distribution network side resources, reduces the prediction error of market resource optimization allocation, and improves the aggregation and collaborative optimization capability of resources;

[0009] During intraday market trading, market demand information and clearing results are iterated multiple times based on the optimized configuration results of the intraday virtual distribution network side. This allows the market to fully consider the volatility and randomness of new energy sources in new power systems with a high proportion of new energy sources, effectively reducing the impact of prediction errors on the market clearing results and resource allocation optimization of the virtual transmission network side.

[0010] By setting up optimized configuration methods on the virtual distribution network side and market-optimized clearing methods on the virtual transmission network side in two phases—one day-ahead and one day-intraday—the virtual distribution network side and the transmission network side can cooperate to optimize resources, fully leveraging the resource regulation capabilities of the virtual distribution network side, improving resource aggregation and collaborative optimization capabilities, and reducing errors in the market clearing results.

[0011] The virtual distribution network side performs market clearing and optimizes the allocation of adjustable resource information based on the day-ahead market declarations to obtain a first optimized allocation result, specifically:

[0012] The virtual distribution network side organizes market clearing based on the electricity demand information from the day-ahead market declarations of the physical distribution network side, with the goal of minimizing electricity purchase costs. Based on the clearing results, it optimizes the adjustable resource information to obtain the first market surplus / shortage and the corresponding first adjustment capacity.

[0013] As a preferred option, in the current market phase, this application uses the principle of optimal resource allocation on the virtual distribution network side. Based on the declared quantity and price of each distributed resource, it internally optimizes the information of adjustable resources, giving full play to the adjustment capability of virtual distribution network side resources, reducing the prediction error of market resource optimization allocation, and improving the aggregation and collaborative optimization capability of resources.

[0014] The step of sending the first optimized configuration result to the first transmission network side market clearing model, so that the virtual transmission network side can submit applications and clear, and obtain the first clearing result, specifically involves:

[0015] The results of the first market surplus / shortage and the corresponding first regulation capacity are sent to the virtual transmission network side as the first optimization configuration result. The virtual transmission network side uses the safety-constrained economic dispatch algorithm to perform centralized optimization calculation on the first optimization configuration result with the goal of minimizing the total network electricity purchase cost, and obtains the first clearing result. The first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use price and regional price for the operating day.

[0016] As a preferred option, this application, during the day-ahead market phase, uses a safety-constrained economic dispatch algorithm for centralized optimization calculations based on market declarations from the distribution network side, with the goal of minimizing the overall network power purchase cost. This clearing method on the transmission network side optimizes resource calculations based on the distribution network side's optimization allocation results, fully leveraging the regulatory capabilities of virtual distribution network resources, reducing prediction errors in market resource optimization allocation, and improving resource aggregation and collaborative optimization capabilities.

[0017] The virtual distribution network side performs market clearing and optimizes the allocation of adjustable resource information to obtain a second optimized allocation result; the second optimized allocation result is sent to the second transmission network side market clearing model so that the virtual transmission network side can submit applications and clear its market to obtain a second clearing result, specifically:

[0018] Based on the market demand released by the physical transmission network, the virtual distribution network optimizes the adjustable resource information to obtain the results of the second market surplus and shortage and the corresponding second adjustment capacity.

[0019] The results of the second market surplus / shortage and the corresponding second regulation capacity are sent to the virtual transmission network side as the second optimal configuration result. The virtual transmission network side optimizes the second optimal configuration result through a safety-constrained economic dispatch procedure with the objective function of minimizing the total electricity purchase cost, organizes market clearing, and obtains the second clearing result. The second clearing result includes the generation plan, time-of-use price, and zone price of the real-time market bidding transaction.

[0020] As a preferred option, in the intraday market phase, this application uses the principle of optimal resource allocation on the virtual distribution network side, and internally optimizes the information of adjustable resources based on the declared quantity and price of each distributed resource; the clearing method on the virtual transmission network side optimizes the resource calculation based on the optimization allocation results of the distribution network side, giving full play to the adjustment capability of the virtual distribution network side resources, reducing the prediction error of market resource optimization allocation, and improving the aggregation and collaborative optimization capability of resources.

[0021] Prior to the market demand released by the physical transmission network side, it also includes:

[0022] The physical transmission network side conducts market pre-clearing, adjusts the expected reserve demand and issues supply shortage warnings based on the pre-clearing results, and releases market demand based on the adjusted reserve demand and the aforementioned supply shortage warnings.

[0023] As a preferred approach, the transmission network side conducts market pre-clearing, adjusts projected reserve demand and issues supply shortage warnings based on the pre-clearing results, and releases market demand based on the adjusted reserve demand and the supply shortage warnings. This pre-clearing method optimizes the generation and utilization plan on the transmission network side, optimizes resource allocation, and improves resource aggregation and collaborative optimization capabilities.

[0024] The step of iteratively optimizing market demand based on the second clearing result to obtain and output the intraday deployment plan is as follows:

[0025] Obtain the second clearing result, and update the market demand information based on the second clearing result and the adjusted day-ahead generation and consumption plan. Update the clearing result on the virtual transmission network side based on the updated market demand information.

[0026] The market demand information and the clearing results are iteratively updated multiple times until the preset goal of maximizing market economic benefits is achieved, and the updated electricity price information is obtained. The generation and consumption plan is optimized based on real-time electricity consumption information and the updated electricity price information to obtain and output the intraday generation and consumption plan.

[0027] As a preferred option, the virtual distribution network side makes multiple adjustments based on the real-time generation and consumption plan released by the transmission network side. The distribution network side, based on the real-time electricity consumption information of the transmission network side and the price information updated by the main body of the distribution network side, iteratively optimizes its own output to obtain and output the intraday generation and consumption plan with the goal of maximizing its own economic benefits. This allows the market to fully consider the volatility and randomness of new energy in the new power system with a high proportion of new energy, effectively reducing the impact of prediction errors on market clearing results and resource allocation optimization.

[0028] Accordingly, the present invention also provides a multi-level trading device for a virtual power plant, comprising: a day-ahead market optimization clearing module and an intraday market optimization clearing module;

[0029] The day-ahead market optimization and clearing module is used to obtain the day-ahead market declaration information of distributed energy entities on the physical distribution network side during the day-ahead planning stage. This information is then input into the first distribution network side market optimization configuration model, enabling the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information based on the day-ahead market declaration information, thereby obtaining a first optimized configuration result. The first optimized configuration result is then sent to the first transmission network side market clearing model, allowing the virtual transmission network side to submit declarations and clear information, obtain a first clearing result, and adjust the day-ahead generation and consumption plan based on the first clearing result, outputting the adjusted generation and consumption plan.

[0030] During intraday market trading, the optimization and clearing module acquires market demand on the physical transmission network side and inputs this demand into the second distribution network side market optimization configuration model. This enables the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information, obtaining a second optimization configuration result. The second optimization configuration result is then sent to the second transmission network side market clearing model, allowing the virtual transmission network side to submit applications and clear its market, obtaining a second clearing result. Based on the second clearing result, the module iteratively optimizes the market demand, obtains and outputs an intraday consumption plan, and controls the market participants on the virtual distribution network side to execute the intraday consumption plan.

[0031] As a preferred option, the virtual distribution network side market optimization allocation model is constructed in the day-ahead market optimization clearing module and the intraday market optimization clearing module to optimize the virtual distribution network side resources, give full play to the adjustment capability of the virtual distribution network side resources, reduce the prediction error of market resource optimization allocation, and improve the aggregation and collaborative optimization capability of resources.

[0032] During intraday market trading, market demand information and clearing results are iterated multiple times based on the optimized configuration results of the intraday virtual distribution network side. This allows the market to fully consider the volatility and randomness of new energy sources in new power systems with a high proportion of new energy sources, effectively reducing the impact of prediction errors on the market clearing results and resource allocation optimization of the virtual transmission network side.

[0033] By setting up optimized configuration methods on the virtual distribution network side and market-optimized clearing methods on the virtual transmission network side in two phases—one day-ahead and one day-intraday—the virtual distribution network side and the transmission network side can cooperate to optimize resources, fully leveraging the resource regulation capabilities of the virtual distribution network side, improving resource aggregation and collaborative optimization capabilities, and reducing errors in the market clearing results.

[0034] The day-ahead market optimization clearing module includes a first distribution network-side market optimization configuration unit and a first transmission network-side market clearing unit;

[0035] The first distribution network side market optimization allocation unit is used to organize market clearing on the virtual distribution network side based on the electricity demand information of the day-ahead market declaration on the physical distribution network side with the goal of minimizing the electricity purchase cost, and to optimize the adjustable resource information based on the clearing results to obtain the results of the first market surplus and shortage and the corresponding first adjustment capacity.

[0036] The first transmission network-side market clearing unit is used to send the results of the first market surplus / shortage and the corresponding first regulation capacity as the first optimization configuration result to the virtual transmission network side, so that the virtual transmission network side can perform centralized optimization calculation on the first optimization configuration result with the goal of minimizing the total network electricity purchase cost, and obtain the first clearing result by using a safety-constrained economic dispatch algorithm; wherein, the first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use electricity price and regional electricity price for the operating day.

[0037] As a preferred option, in the day-ahead market phase, in the first distribution network side market optimization allocation unit, the virtual distribution network side, based on the principle of optimal resource allocation, internally optimizes the adjustable resource information according to the declared quantity and price of each distributed resource, giving full play to the adjustment capability of the virtual distribution network side resources, reducing the prediction error of market resource optimization allocation, and improving the aggregation and collaborative optimization capability of resources.

[0038] In the first transmission network-side market clearing unit, the transmission network side, based on the market declarations from the distribution network side, uses the lowest overall network power purchase cost as the optimization objective and employs a safety-constrained economic dispatch algorithm for centralized optimization calculations. This transmission network-side clearing method, by optimizing resource calculations based on the distribution network side's optimization allocation results, fully leverages the regulatory capabilities of virtual distribution network resources, reduces prediction errors in market resource optimization allocation, and improves resource aggregation and collaborative optimization capabilities.

[0039] The intraday market optimization clearing module includes a second distribution network-side market optimization configuration unit, a second transmission network-side market clearing unit, and an iterative optimization unit;

[0040] The second distribution network side market optimization configuration unit is used to optimize the adjustable resource information on the virtual distribution network side according to the market demand released by the physical transmission network side, and obtain the results of the second market surplus and shortage and the corresponding second adjustment capacity.

[0041] The second transmission grid-side market clearing unit is used to send the results of the second market surplus / shortage and the corresponding second regulation capacity as the second optimal configuration result to the virtual transmission grid side, so that the virtual transmission grid side optimizes the second optimal configuration result with the objective function of minimizing the total electricity purchase cost, organizes market clearing, and obtains the second clearing result. The second clearing result includes the generation plan, time-of-use price and regional price of the real-time market bidding transaction.

[0042] The iterative optimization unit is used to obtain the second clearing result, and update the market demand information according to the second clearing result and the adjusted day-ahead generation and consumption plan. According to the updated market demand information, it updates the clearing result on the virtual transmission network side. The market demand information and the clearing result are iteratively updated multiple times until the preset target of maximizing market economic benefits is reached, and the updated electricity price information is obtained. The generation and consumption plan is optimized according to the real-time electricity consumption information and the updated electricity price information to obtain and output the intraday generation and consumption plan.

[0043] As a preferred option, in the intraday market phase, within the second distribution network side market optimization allocation unit and the second transmission network side market clearing unit, the virtual distribution network side, based on the principle of optimal resource allocation, internally optimizes the information of adjustable resources according to the declared quantity and price of each distributed resource; the clearing method of the virtual transmission network side optimizes the resource calculation based on the optimization allocation results of the distribution network side, giving full play to the adjustment capability of the virtual distribution network side resources, reducing the prediction error of market resource optimization allocation, and improving the aggregation and collaborative optimization capability of resources.

[0044] In the iterative optimization unit, the virtual distribution network side makes multiple adjustments based on the real-time generation and consumption plan released by the transmission network side. The distribution network side, based on the real-time electricity consumption information of the transmission network side and the price information updated by the main body of the distribution network side, iteratively optimizes its own output to obtain and output the intraday generation and consumption plan with the goal of maximizing its own economic benefits. This allows the market to fully consider the volatility and randomness of new energy in the new power system with a high proportion of new energy, effectively reducing the impact of prediction errors on market clearing results and resource allocation optimization.

[0045] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a multi-tiered trading method for a virtual power plant as described in the present invention. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating an embodiment of the multi-level trading method for virtual power plants provided by the present invention;

[0047] Figure 2 This is a flowchart illustrating another embodiment of the multi-level trading method for virtual power plants provided by the present invention.

[0048] Figure 3 This is a flowchart illustrating another embodiment of the multi-level trading method for virtual power plants provided by the present invention;

[0049] Figure 4 This is a schematic diagram of the structure of one embodiment of the multi-level trading device for a virtual power plant provided by the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] Please refer to Figure 1 This invention provides a multi-level trading method for a virtual power plant, wherein the virtual power plant includes a virtual distribution network side and a virtual transmission network side, and is applied to the virtual distribution network side, including steps S101-S102:

[0053] Step S101: During the day-ahead planning phase, obtain the day-ahead market declaration information of the distributed energy entities on the physical distribution network side, input the day-ahead market declaration information into the first distribution network side market optimization configuration model, so that the virtual distribution network side can perform market clearing and optimize the configuration of adjustable resource information based on the day-ahead market declaration information to obtain a first optimization configuration result; send the first optimization configuration result to the first transmission network side market clearing model, so that the virtual transmission network side can submit and clear, obtain a first clearing result, and adjust the day-ahead generation and consumption plan based on the first clearing result, and output the adjusted generation and consumption plan.

[0054] In this embodiment, the first distribution network-side market optimization allocation model includes an objective function and constraints for the optimization allocation model, specifically:

[0055] Optimize the objective function of the configuration model:

[0056]

[0057] In the formula, The purchase price or sale price of electricity set by the power dispatching agency on the distribution network side; ω is the subsidy price set to encourage users to participate in demand response; ΔP t D P represents the amount of load reduced by market participants on the distribution network side through demand response at time t. t D P represents the load value of the market participants on the distribution network side at time t. v,t To contribute to energy sources such as electric vehicles and distributed energy storage; To achieve a unified clearing price for the electricity market; P t W This represents the amount of electricity purchased by the distribution network from the electricity market at time t.

[0058] The constraints of the optimized configuration model are the power balance constraints of each market entity and branch.

[0059] In this embodiment, the first transmission network-side market clearing model includes a clearing model objective function and clearing model constraints, specifically:

[0060] Clearing model objective function:

[0061]

[0062] In the formula, N represents the total number of generating units, including non-market and market generating units; T represents the total number of time periods considered, where each time period on day D is 15 minutes long, considering 96 time periods; and on day D+1, two time periods are considered, namely peak and off-peak loads, so T is 98; P i,t This represents the output of unit i during time period t; C i,t (P i,t ) represents the operating cost of unit i in time period t, which is a multi-segment linear function related to the output ranges declared by the unit and the corresponding energy prices; M represents the network flow constraint relaxation penalty factor used for market clearing optimization; represents the forward and reverse power flow relaxation variables of line l, respectively; NL represents the total number of lines; represents the forward and reverse current relaxation variables of section s, respectively; NS represents the total number of sections;

[0063] The constraints of the clearing model include system load balance constraints, unit output upper and lower limit constraints, unit ramping constraints, and line power flow constraints;

[0064] System load balancing constraints:

[0065]

[0066] In the formula, represents the planned power of tie line j in time period t, NT represents the total number of tie lines, and Dt represents the system load in time period t.

[0067] Unit output upper and lower limit constraints:

[0068]

[0069] In the formula, These are the upper and lower limits of the output of the i-th unit, respectively;

[0070] Unit ramp-up constraints:

[0071]

[0072] In the formula, P i up P i down These are the upward and downward ramp rate limits for the i-th unit, respectively;

[0073] Power flow constraints on the line:

[0074]

[0075] Among them, P l max G represents the power flow transmission limit of line l; i-1 G represents the generator output power transfer distribution factor from node i to line l; l-j The generator output power transfer distribution factor of the node containing tie line j to line l is represented by: K; the number of nodes in the system is represented by: G l-k D represents the generator output power transfer distribution factor from node k to line l. k,t This represents the bus load value of node k during time period t.

[0076] In this embodiment, the virtual distribution network side performs market clearing and optimizes the allocation of adjustable resource information based on the day-ahead market declarations to obtain a first optimized allocation result, specifically:

[0077] The virtual distribution network side organizes market clearing based on the electricity demand information from the day-ahead market declarations of the physical distribution network side, with the goal of minimizing electricity purchase costs. Based on the clearing results, it optimizes the adjustable resource information to obtain the first market surplus / shortage and the corresponding first adjustment capacity.

[0078] The first optimized configuration result is sent to the first transmission network side market clearing model so that the virtual transmission network side can submit applications and clear, and obtain the first clearing result, specifically:

[0079] The results of the first market surplus / shortage and the corresponding first regulation capacity are sent to the virtual transmission network side as the first optimization configuration result. The virtual transmission network side uses the safety-constrained economic dispatch algorithm to perform centralized optimization calculation on the first optimization configuration result with the goal of minimizing the total network electricity purchase cost, and obtains the first clearing result. The first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use price and regional price for the operating day.

[0080] Step S102: During intraday market trading, obtain the market demand on the physical transmission network side, input the market demand into the second distribution network side market optimization configuration model, so that the virtual distribution network side can perform market clearing and optimize the configuration of adjustable resource information to obtain a second optimization configuration result; send the second optimization configuration result to the second transmission network side market clearing model, so that the virtual transmission network side can submit and clear, to obtain a second clearing result; iteratively optimize the market demand based on the second clearing result, obtain and output the intraday consumption plan, and control the market participants on the virtual distribution network side to execute the intraday consumption plan.

[0081] In this embodiment, the second distribution network side market optimization allocation model is the same as the first distribution network side market optimization allocation model; wherein, the objective function and constraints of the optimization allocation model of the second distribution network side market optimization allocation model are the same as those of the first distribution network side market optimization allocation model.

[0082] In this embodiment, the second transmission network-side market clearing model is the same as the first transmission network-side market clearing model; wherein, the objective function and constraints of the clearing model of the second transmission network-side market clearing model are the same as those of the first transmission network-side market clearing model.

[0083] In this embodiment, the virtual distribution network side performs market clearing and optimizes the allocation of adjustable resource information to obtain a second optimized allocation result; the second optimized allocation result is sent to the second transmission network side market clearing model so that the virtual transmission network side can submit applications and clear, obtaining a second clearing result, specifically:

[0084] Based on the market demand released by the physical transmission network, the virtual distribution network optimizes the adjustable resource information to obtain the results of the second market surplus and shortage and the corresponding second adjustment capacity.

[0085] The results of the second market surplus / shortage and the corresponding second regulation capacity are sent to the virtual transmission network side as the second optimal configuration result. The virtual transmission network side optimizes the second optimal configuration result through a safety-constrained economic dispatch procedure with the objective function of minimizing the total electricity purchase cost, organizes market clearing, and obtains the second clearing result. The second clearing result includes the generation plan, time-of-use price, and zone price of the real-time market bidding transaction.

[0086] In this embodiment, the market demand is iteratively optimized based on the second clearing result to obtain and output the intraday deployment plan, specifically as follows:

[0087] Obtain the second clearing result, and update the market demand information based on the second clearing result and the adjusted day-ahead generation and consumption plan. Update the clearing result on the virtual transmission network side based on the updated market demand information.

[0088] The market demand information and the clearing results are iteratively updated multiple times until the preset goal of maximizing market economic benefits is achieved, and the updated electricity price information is obtained. The generation and consumption plan is optimized based on real-time electricity consumption information and the updated electricity price information to obtain and output the intraday generation and consumption plan.

[0089] Implementing this embodiment has the following beneficial effects:

[0090] This application constructs a virtual distribution network side market optimization allocation model during the day-ahead and intraday market filing process, optimizes virtual distribution network side resources, fully leverages the adjustment capabilities of virtual distribution network side resources, reduces the prediction error of market resource optimization allocation, and improves the aggregation and collaborative optimization capabilities of resources;

[0091] During intraday market trading, market demand information and clearing results are iterated multiple times based on the optimized configuration results of the intraday virtual distribution network side. This allows the market to fully consider the volatility and randomness of new energy sources in new power systems with a high proportion of new energy sources, effectively reducing the impact of prediction errors on the market clearing results and resource allocation optimization of the virtual transmission network side.

[0092] By setting up optimized configuration methods on the virtual distribution network side and market-optimized clearing methods on the virtual transmission network side in two phases—one day-ahead and one day-intraday—the virtual distribution network side and the transmission network side can cooperate to optimize resources, fully leveraging the resource regulation capabilities of the virtual distribution network side, improving resource aggregation and collaborative optimization capabilities, and reducing errors in the market clearing results.

[0093] Example 2

[0094] Please refer to Figure 2This invention provides a multi-level trading method for a virtual power plant, wherein the virtual power plant includes a virtual distribution network side and a virtual transmission network side, and is applied to the virtual transmission network side, including steps S201-S202:

[0095] Step S201: In the day-ahead planning stage, obtain the first optimized configuration result output by the first distribution network side market optimized configuration model; input the first optimized configuration result into the first transmission network side market clearing model so that the virtual transmission network side can submit and clear; send the first clearing result to the virtual distribution network side so that the virtual distribution network side can adjust the day-ahead generation and consumption plan according to the first clearing result and output the adjusted generation and consumption plan.

[0096] In this embodiment, the first distribution network-side market optimization allocation model includes an objective function and constraints for the optimization allocation model, specifically:

[0097] Optimize the objective function of the configuration model:

[0098]

[0099] In the formula, The purchase price or sale price of electricity set by the power dispatching agency on the distribution network side; ω is the subsidy price set to encourage users to participate in demand response; ΔP t D P represents the amount of load reduced by market participants on the distribution network side through demand response at time t. t D P represents the load value of the market participants on the distribution network side at time t. v,t To contribute to energy sources such as electric vehicles and distributed energy storage; To achieve a unified clearing price for the electricity market; P t W This represents the amount of electricity purchased by the distribution network from the electricity market at time t.

[0100] The constraints of the optimized configuration model are the power balance constraints of each market entity and branch.

[0101] In this embodiment, the first transmission network-side market clearing model includes a clearing model objective function and clearing model constraints, specifically:

[0102] Clearing model objective function:

[0103]

[0104] In the formula, N represents the total number of generating units, including non-market and market generating units; T represents the total number of time periods considered, where each time period on day D is 15 minutes long, considering 96 time periods; and on day D+1, two time periods are considered, namely peak and off-peak loads, so T is 98; Pi,t This represents the output of unit i during time period t; C i,t (P i,t ) represents the operating cost of unit i in time period t, which is a multi-segment linear function related to the output ranges declared by the unit and the corresponding energy prices; M represents the network flow constraint relaxation penalty factor used for market clearing optimization; represents the forward and reverse power flow relaxation variables of line l, respectively; NL represents the total number of lines; represents the forward and reverse current relaxation variables of section s, respectively; NS represents the total number of sections;

[0105] The constraints of the clearing model include system load balance constraints, unit output upper and lower limit constraints, unit ramping constraints, and line power flow constraints;

[0106] System load balancing constraints:

[0107]

[0108] In the formula, represents the planned power of tie line j in time period t, NT represents the total number of tie lines, and Dt represents the system load in time period t.

[0109] Unit output upper and lower limit constraints:

[0110]

[0111] In the formula, These are the upper and lower limits of the output of the i-th unit, respectively;

[0112] Unit ramp-up constraints:

[0113]

[0114] In the formula, P i up P i down These are the upward and downward ramp rate limits for the i-th unit, respectively;

[0115] Power flow constraints on the line:

[0116]

[0117] Among them, P l max G represents the power flow transmission limit of line l; i-1 G represents the generator output power transfer distribution factor from node i to line l; l-j The generator output power transfer distribution factor of the node containing tie line j to line l is represented by: K; the number of nodes in the system is represented by: G l-kD represents the generator output power transfer distribution factor from node k to line l. k,t This represents the bus load value of node k during time period t.

[0118] In this embodiment, the first optimized configuration result is input into the first transmission network side market clearing model so that the virtual transmission network side can perform declaration and clearing, specifically:

[0119] To obtain the first optimized configuration result, the virtual transmission network side uses the lowest overall network electricity purchase cost as the optimization objective and employs a safety-constrained economic dispatch algorithm to perform centralized optimization calculations on the first optimized configuration result to obtain the first clearing result. The first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use electricity price, and zoned electricity price for the operating day.

[0120] Step S202: During intraday market trading, acquire market demand on the physical transmission network side and send the market demand to the second distribution network side market optimization configuration model so that the virtual distribution network side can perform market clearing and optimize the configuration of adjustable resource information to obtain a second optimization configuration result; acquire the second optimization configuration result and input the second optimization configuration result into the second transmission network side market clearing model so that the virtual transmission network side can submit and clear, to obtain a second clearing result; send the second clearing result to the virtual distribution network side so that the virtual distribution network side can iteratively optimize the market demand based on the second clearing result, obtain and output the intraday consumption plan, and control the market participants on the virtual distribution network side to execute the intraday consumption plan.

[0121] In this embodiment, the second distribution network side market optimization allocation model is the same as the first distribution network side market optimization allocation model; wherein, the objective function and constraints of the optimization allocation model of the second distribution network side market optimization allocation model are the same as those of the first distribution network side market optimization allocation model.

[0122] In this embodiment, the second transmission network-side market clearing model is the same as the first transmission network-side market clearing model; wherein, the objective function and constraints of the clearing model of the second transmission network-side market clearing model are the same as those of the first transmission network-side market clearing model.

[0123] In this embodiment, the second optimized configuration result is input into the second transmission network side market clearing model so that the virtual transmission network side can submit applications and clear, thereby obtaining the second clearing result, specifically:

[0124] To obtain the second optimal configuration result, the virtual transmission network side uses the minimization of total electricity purchase cost as the objective function and optimizes the second optimal configuration result through a safety-constrained economic dispatch program. It then organizes market clearing to obtain the second clearing result, which includes the generation plan, time-of-use price, and zone price of the real-time market bidding transaction.

[0125] In this embodiment, prior to the market demand published by the physical transmission network side, the following is also included:

[0126] The physical transmission network side conducts market pre-clearing, adjusts the expected reserve demand and issues supply shortage warnings based on the pre-clearing results, and releases market demand based on the adjusted reserve demand and the aforementioned supply shortage warnings.

[0127] Reference Figure 3 This is a schematic diagram of the overall process of the multi-level trading method for virtual power plants provided by the present invention.

[0128] Implementing this embodiment has the following beneficial effects:

[0129] This application constructs a virtual distribution network side market optimization allocation model during the day-ahead and intraday market filing process, optimizes virtual distribution network side resources, fully leverages the adjustment capabilities of virtual distribution network side resources, reduces the prediction error of market resource optimization allocation, and improves the aggregation and collaborative optimization capabilities of resources;

[0130] During intraday market trading, market demand information and clearing results are iterated multiple times based on the optimized configuration results of the intraday virtual distribution network side. This allows the market to fully consider the volatility and randomness of new energy sources in new power systems with a high proportion of new energy sources, effectively reducing the impact of prediction errors on the market clearing results and resource allocation optimization of the virtual transmission network side.

[0131] By setting up optimized configuration methods on the virtual distribution network side and market-optimized clearing methods on the virtual transmission network side in two phases—one day-ahead and one day-intraday—the virtual distribution network side and the transmission network side can cooperate to optimize resources, fully leveraging the resource regulation capabilities of the virtual distribution network side, improving resource aggregation and collaborative optimization capabilities, and reducing errors in the market clearing results.

[0132] Example 3

[0133] Please refer to Figure 4 A multi-level trading device for a virtual power plant provided in this embodiment of the invention includes: a day-ahead market optimization clearing module 301 and an intraday market optimization clearing module 302;

[0134] The day-ahead market optimization and clearing module 301 is used to obtain the day-ahead market declaration information of distributed energy entities on the physical distribution network side during the day-ahead planning stage, input the day-ahead market declaration information into the first distribution network side market optimization configuration model, so that the virtual distribution network side can perform market clearing and optimize the configuration of adjustable resource information based on the day-ahead market declaration information to obtain a first optimization configuration result; send the first optimization configuration result to the first transmission network side market clearing model, so that the virtual transmission network side can submit and clear, obtain a first clearing result, and adjust the day-ahead generation and consumption plan based on the first clearing result, and output the adjusted generation and consumption plan;

[0135] The day-ahead market optimization clearing module includes a first distribution network-side market optimization configuration unit and a first transmission network-side market clearing unit;

[0136] The first distribution network side market optimization allocation unit is used to organize market clearing on the virtual distribution network side based on the electricity demand information of the day-ahead market declaration on the physical distribution network side with the goal of minimizing the electricity purchase cost, and to optimize the adjustable resource information based on the clearing results to obtain the results of the first market surplus and shortage and the corresponding first adjustment capacity.

[0137] The first transmission network-side market clearing unit is used to send the results of the first market surplus / shortage and the corresponding first regulation capacity as the first optimization configuration result to the virtual transmission network side, so that the virtual transmission network side can perform centralized optimization calculation on the first optimization configuration result with the goal of minimizing the total network electricity purchase cost, and obtain the first clearing result by using a safety-constrained economic dispatch algorithm; wherein, the first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use electricity price and regional electricity price for the operating day.

[0138] The intraday market optimization clearing module includes a second distribution network-side market optimization configuration unit, a second transmission network-side market clearing unit, and an iterative optimization unit;

[0139] The second distribution network side market optimization configuration unit is used to optimize the adjustable resource information on the virtual distribution network side according to the market demand released by the physical transmission network side, and obtain the results of the second market surplus and shortage and the corresponding second adjustment capacity.

[0140] During intraday market trading, the intraday market optimization clearing module 302 acquires market demand on the physical transmission network side and inputs this market demand into the second distribution network side market optimization configuration model. This enables the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information, obtaining a second optimization configuration result. The second optimization configuration result is then sent to the second transmission network side market clearing model, allowing the virtual transmission network side to submit applications and clear its market, obtaining a second clearing result. Based on the second clearing result, the market demand is iteratively optimized to obtain and output an intraday consumption plan, controlling the market participants on the virtual distribution network side to execute the intraday consumption plan.

[0141] The second transmission grid-side market clearing unit is used to send the results of the second market surplus / shortage and the corresponding second regulation capacity as the second optimal configuration result to the virtual transmission grid side, so that the virtual transmission grid side optimizes the second optimal configuration result with the objective function of minimizing the total electricity purchase cost, organizes market clearing, and obtains the second clearing result. The second clearing result includes the generation plan, time-of-use price and regional price of the real-time market bidding transaction.

[0142] The iterative optimization unit is used to obtain the second clearing result, and update the market demand information according to the second clearing result and the adjusted day-ahead generation and consumption plan. According to the updated market demand information, it updates the clearing result on the virtual transmission network side. The market demand information and the clearing result are iteratively updated multiple times until the preset target of maximizing market economic benefits is reached, and the updated electricity price information is obtained. The generation and consumption plan is optimized according to the real-time electricity consumption information and the updated electricity price information to obtain and output the intraday generation and consumption plan.

[0143] The aforementioned multi-tiered trading device for virtual power plants can implement the multi-tiered trading method for virtual power plants described in the above method embodiments. The options described in the above method embodiments are also applicable to this embodiment and will not be detailed here. The remaining content of this application's embodiments can be found in the content of the above method embodiments, and will not be repeated in this embodiment.

[0144] This embodiment has the following beneficial effects:

[0145] The virtual distribution network side market optimization allocation model was constructed in the recent market optimization clearing module and the intraday market optimization clearing module. This model optimizes the virtual distribution network side resources, fully leverages the adjustment capabilities of the virtual distribution network side resources, reduces the prediction error of market resource optimization allocation, and improves the aggregation and collaborative optimization capabilities of resources.

[0146] During intraday market trading, market demand information and clearing results are iterated multiple times based on the optimized configuration results of the intraday virtual distribution network side. This allows the market to fully consider the volatility and randomness of new energy sources in new power systems with a high proportion of new energy sources, effectively reducing the impact of prediction errors on the market clearing results and resource allocation optimization of the virtual transmission network side.

[0147] By setting up optimized configuration methods on the virtual distribution network side and market-optimized clearing methods on the virtual transmission network side in two phases—one day-ahead and one day-intraday—the virtual distribution network side and the transmission network side can cooperate to optimize resources, fully leveraging the resource regulation capabilities of the virtual distribution network side, improving resource aggregation and collaborative optimization capabilities, and reducing errors in the market clearing results.

[0148] Example 4

[0149] Accordingly, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the multi-level trading method of the virtual power plant as described in any of the above embodiments.

[0150] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0151] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0152] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0153] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0154] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0155] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A multi-tiered trading method for a virtual power plant, characterized in that, The virtual power plant includes a virtual distribution network side and a virtual transmission network side, and the multi-level trading method includes: During the day-ahead planning phase, the day-ahead market declaration information of distributed energy entities on the physical distribution network side is obtained. This information is then input into the first distribution network-side market optimization allocation model, enabling the virtual distribution network side to perform market clearing and optimize the allocation of adjustable resource information based on the day-ahead market declaration information, thereby obtaining a first optimized allocation result. The first optimized allocation result is then sent to the first transmission network-side market clearing model, allowing the virtual transmission network side to submit declarations and clear, obtain a first clearing result, and adjust the day-ahead generation and consumption plan based on the first clearing result, outputting the adjusted generation and consumption plan. During intraday market trading, market demand on the physical transmission network side is acquired and input into the second distribution network side market optimization configuration model. This enables the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information, obtaining a second optimization configuration result. The second optimization configuration result is then sent to the second transmission network side market clearing model, allowing the virtual transmission network side to submit applications and clear its market, obtaining a second clearing result. Based on the second clearing result, market demand is iteratively optimized to obtain and output an intraday consumption plan, controlling the market participants on the virtual distribution network side to execute the intraday consumption plan. The virtual distribution network side performs market clearing and optimizes the allocation of adjustable resource information to obtain a second optimized allocation result; the second optimized allocation result is sent to the second transmission network side market clearing model so that the virtual transmission network side can submit applications and clear its market to obtain a second clearing result, specifically: Based on the market demand released by the physical transmission network, the virtual distribution network optimizes the adjustable resource information to obtain the results of the second market surplus and shortage and the corresponding second adjustment capacity. The results of the second market surplus / shortage and the corresponding second regulation capacity are sent to the virtual transmission network side as the second optimal configuration result. The virtual transmission network side optimizes the second optimal configuration result through a safety-constrained economic dispatch procedure with the objective function of minimizing the total electricity purchase cost, organizes market clearing, and obtains the second clearing result. The second clearing result includes the generation plan, time-of-use price, and zone price of the real-time market bidding transaction.

2. The multi-level trading method for virtual power plants as described in claim 1, characterized in that, The virtual distribution network side performs market clearing and optimizes the allocation of adjustable resource information based on the day-ahead market declarations to obtain a first optimized allocation result, specifically: The virtual distribution network side organizes market clearing based on the electricity demand information from the day-ahead market declarations of the physical distribution network side, with the goal of minimizing electricity purchase costs. Based on the clearing results, it optimizes the adjustable resource information to obtain the first market surplus / shortage and the corresponding first adjustment capacity.

3. The multi-level trading method for virtual power plants as described in claim 2, characterized in that, The step of sending the first optimized configuration result to the first transmission network side market clearing model, so that the virtual transmission network side can submit applications and clear, and obtain the first clearing result, specifically involves: The results of the first market surplus / shortage and the corresponding first regulation capacity are sent to the virtual transmission network side as the first optimization configuration result. The virtual transmission network side uses the safety-constrained economic dispatch algorithm to perform centralized optimization calculation on the first optimization configuration result with the goal of minimizing the total network electricity purchase cost, and obtains the first clearing result. The first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use price and regional price for the operating day.

4. The multi-level trading method for virtual power plants as described in claim 1, characterized in that, Prior to the market demand released by the physical transmission network side, it also includes: The physical transmission network side conducts market pre-clearing, adjusts the expected reserve demand and issues supply shortage warnings based on the pre-clearing results, and releases market demand based on the adjusted reserve demand and the aforementioned supply shortage warnings.

5. The multi-level trading method for virtual power plants as described in claim 1, characterized in that, The step of iteratively optimizing market demand based on the second clearing result to obtain and output the intraday deployment plan is as follows: Obtain the second clearing result, and update the market demand information based on the second clearing result and the adjusted day-ahead generation and consumption plan. Update the clearing result on the virtual transmission network side based on the updated market demand information. The market demand information and the clearing results are iteratively updated multiple times until the preset goal of maximizing market economic benefits is achieved, and the updated electricity price information is obtained. The generation and consumption plan is optimized based on real-time electricity consumption information and the updated electricity price information to obtain and output the intraday generation and consumption plan.

6. A multi-level trading device for a virtual power plant, characterized in that, include: The daytime market clearing module and the intraday market clearing module; The day-ahead market optimization and clearing module is used to obtain the day-ahead market declaration information of distributed energy entities on the physical distribution network side during the day-ahead planning stage. This information is then input into the first distribution network side market optimization configuration model, enabling the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information based on the day-ahead market declaration information, thereby obtaining a first optimized configuration result. The first optimized configuration result is then sent to the first transmission network side market clearing model, allowing the virtual transmission network side to submit declarations and clear information, obtain a first clearing result, and adjust the day-ahead generation and consumption plan based on the first clearing result, outputting the adjusted generation and consumption plan. During intraday market trading, the optimization and clearing module acquires market demand on the physical transmission network side and inputs this demand into the second distribution network side market optimization configuration model. This enables the virtual distribution network side to perform market clearing and optimize the configuration of adjustable resource information, obtaining a second optimization configuration result. The second optimization configuration result is then sent to the second transmission network side market clearing model, allowing the virtual transmission network side to submit applications and clear its market, obtaining a second clearing result. Based on the second clearing result, the module iteratively optimizes the market demand, obtains and outputs an intraday consumption plan, and controls the market participants on the virtual distribution network side to execute the intraday consumption plan. The day-ahead market optimization clearing module includes a first distribution network-side market optimization configuration unit and a first transmission network-side market clearing unit; The first distribution network side market optimization allocation unit is used to organize market clearing on the virtual distribution network side based on the electricity demand information of the day-ahead market declaration on the physical distribution network side with the goal of minimizing the electricity purchase cost, and to optimize the adjustable resource information based on the clearing results to obtain the results of the first market surplus and shortage and the corresponding first adjustment capacity. The second transmission grid-side market clearing unit is used to send the results of the second market surplus / shortage and the corresponding second regulation capacity as the second optimal configuration result to the virtual transmission grid side, so that the virtual transmission grid side optimizes the second optimal configuration result with the objective function of minimizing the total electricity purchase cost, organizes market clearing, and obtains the second clearing result. The second clearing result includes the generation plan, time-of-use price and regional price of the real-time market bidding transaction.

7. The multi-level trading device for a virtual power plant as described in claim 6, characterized in that, The first transmission network-side market clearing unit is also used to send the results of the first market surplus / shortage and the corresponding first regulation capacity as the first optimization configuration result to the virtual transmission network side, so that the virtual transmission network side takes the lowest power purchase cost of the entire network as the optimization objective and uses the safety-constrained economic dispatch algorithm to perform centralized optimization calculation on the first optimization configuration result to obtain the first clearing result; wherein, the first clearing result includes the unit start-up and shutdown plan, power generation output curve, time-of-use price and regional price for the operating day.

8. The multi-level trading device for a virtual power plant as described in claim 6, characterized in that, The intraday market optimization clearing module also includes an iterative optimization unit; The iterative optimization unit is used to obtain the second clearing result, update the market demand information according to the second clearing result and the adjusted day-ahead generation and consumption plan, and update the clearing result of the virtual transmission network side according to the updated market demand information. The market demand information and the clearing results are iteratively updated multiple times until the preset goal of maximizing market economic benefits is achieved, and the updated electricity price information is obtained. The generation and consumption plan is optimized based on real-time electricity consumption information and the updated electricity price information to obtain and output the intraday generation and consumption plan.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a multi-tiered trading method for a virtual power plant as described in any one of claims 1 to 5.

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