Green electricity short-time balance transaction scheduling method and device based on resource capacity equivalence

Abstract

The application discloses a green electricity short-time balance transaction scheduling method and device based on resource capacity equivalence, wherein the method comprises the following steps: based on the related prediction information provided by the power dispatching department, the demand capacity is determined by using the supply-demand deviation of new energy and power users, the pre-dispatching capacity of the virtual power plant is determined, the bidding strategy of the 96-point plan operation curve of the virtual power plant and the multi-section type quantity-price curve is constructed; the virtual power plant economic dispatching model is constructed according to the quantity-price curve declared by the virtual power plant, and the clearing result of the virtual power plant is formed; the virtual power plant is settled according to the clearing price and the winning capacity, and the highest apportioned cost that the green power user is willing to pay for the green energy premium is considered, and the adjustment cost generated in the operation process of the virtual power plant is apportioned. Therefore, the technical problem that the virtual power plant is difficult to formulate a capacity contract delivery target for the operation characteristics of different types of distributed resources and the attributes of user equipment utility in the related art is solved.

Description

Technical Field

[0001] This application relates to the field of power market technical support system technology, and in particular to a method and apparatus for green electricity short-term balance trading and scheduling based on resource capacity equivalence. Background Technology

[0002] Driven by both energy transition and power reform, the efficient and reliable supply of energy and the construction of market mechanisms have become key engines for the large-scale and orderly development of new energy. However, the operational deviations caused by the volatility and randomness of new energy output and the diversity and sparsity of energy demand from green electricity users not only bring deviation settlement pressure to market players, but also pose higher requirements for the power system and market mechanisms due to the operational risks brought about by the real-time supply and demand balance of the power system.

[0003] Virtual power plants, acting as a balance in the power supply and demand balance, can leverage advanced power Internet of Things (IoT) technology to aggregate and regulate distributed resources, flexibly adjusting the supply and demand imbalance between generation and consumption, and effectively supporting the balance of green power supply and demand.

[0004] In related technologies, virtual power plants can utilize the dual characteristics of generation and consumption of storage and energy storage devices to serve both the generation and consumption sides. They can provide capacity adjustment for both the generation and consumption market entities through capacity contracts, dynamically adjusting the supply and demand deviation. In essence, the capacity contract uses the control of flexible and adjustable capacity as the trading object. The signing of the contract means the transfer of the control of the flexible resources aggregated by the virtual power plant.

[0005] However, in related technologies, when selecting control targets for capacity contract design, due to the lack of basic knowledge about electricity among users, the regulation and optimization with power capacity as the control target is not conducive to mobilizing the enthusiasm of users to participate in resource allocation. This makes it difficult for virtual power plants to formulate capacity contract delivery targets based on the operating characteristics of different types of distributed resources and the attributes of user equipment utility. This is not conducive to reducing the pressure of deviation settlement and mitigating the operational risks brought about by the real-time supply and demand balance of the power system, and needs to be improved. Summary of the Invention

[0006] This application provides a method and apparatus for green electricity short-term balance trading and scheduling based on resource capacity equivalence, in order to solve the technical problem in related technologies that the regulation and optimization with power capacity as the control target is not conducive to mobilizing the enthusiasm of users to participate in resource allocation, and makes it difficult for virtual power plants to formulate capacity contract delivery targets for different types of distributed resource operation characteristics and user equipment utility attributes.

[0007] The first aspect of this application provides a method for short-term balance trading and dispatching of green electricity based on resource capacity equivalence, comprising the following steps: determining the demand capacity based on relevant forecast information provided by the power dispatching department using the supply-demand deviation between new energy sources and power users, and determining the pre-dispatch capacity of virtual power plants based on the demand capacity; constructing a bidding strategy for a 96-point planned operation curve and a multi-segment quantity-price curve for the virtual power plant's operating days based on the pre-dispatch capacity of the virtual power plant; constructing an economic dispatch model for the virtual power plant based on the quantity-price curve declared by the virtual power plant, with the minimization of operating costs as the objective function, and solving the model to form the clearing result of the virtual power plant, wherein the clearing result includes the winning bid capacity and clearing price of the virtual power plant; settling accounts with the virtual power plant based on the clearing price and the winning bid capacity, and allocating the costs to green electricity users according to a preset green energy consumption ratio, while considering the maximum allocation fee that green electricity users are willing to pay for the green energy premium, and diverting the costs exceeding the green energy premium to green electricity enterprises according to a preset power generation ratio, thereby allocating the adjustment costs generated during the operation of the virtual power plant.

[0008] Optionally, in one embodiment of this application, the objective function is:

[0009]

[0010] Where I represents the number of virtual power plants, and t represents the time point of the 96-point planned operation curve of the virtual power plants. Let be the charge / discharge state coefficient of the i-th virtual power plant at time t. Let i be the upward adjustment capacity reported in real time by the i-th virtual power plant at time t. Let be the adjusted price declared in real time by the i-th virtual power plant at time t. This represents the downward adjustment capacity reported in real time by the i-th virtual power plant at time t.

[0011] Optionally, in one embodiment of this application, determining the pre-scheduled capacity of the virtual power plant based on the demand capacity includes: when the difference between the predicted output of new energy sources and the predicted demand of green electricity users on the operating day is greater than or equal to a preset threshold, the virtual power plant does not need to generate a pre-scheduled plan; when the difference between the predicted output of new energy sources and the predicted demand of green electricity users on the operating day is less than the preset threshold, while organizing the virtual power plant to conduct transactions, it is determined whether the flexible adjustable capacity of the aggregated resources of the virtual power plant meets the preset conditions.

[0012] Optionally, in one embodiment of this application, after determining whether the flexible resource adjustable capacity aggregated by the virtual power plant meets the preset conditions, the method further includes: if the preset conditions are met, requesting off-site support from the dispatch center; if the preset conditions are not met, generating a pre-call plan for the virtual power plant.

[0013] Optionally, in one embodiment of this application, the bidding strategy for constructing a 96-point planned operation curve and a multi-segment quantity-price curve for the virtual power plant based on the pre-scheduled capacity of the virtual power plant includes: dividing the adjustable capacity according to the pricing method stipulated by the power trading center based on the self-regulating capacity of the virtual power plant, and obtaining the aggregated resource response allocation for each segment; confirming the data of the forecast of renewable energy output and the forecast of green electricity user demand for the operation day, and publishing the forecast results and the upper limit of the price of the virtual power plant to each market participant.

[0014] A second aspect of this application provides a green electricity short-term balance trading and dispatching device based on resource capacity equivalence, comprising: a first calculation module, used to determine the demand capacity based on relevant forecast information provided by the power dispatching department, utilizing the supply-demand deviation between new energy and power users, and determining the pre-dispatch capacity of a virtual power plant according to the demand capacity; a construction module, used to construct a bidding strategy for a 96-point planned operation curve and a multi-segment quantity-price curve for the virtual power plant's operating days based on the pre-dispatch capacity of the virtual power plant; and a second calculation module, used to calculate the operating cost based on the bidding strategy, according to the quantity-price curve declared by the virtual power plant. Minimize the objective function to construct an economic dispatch model for virtual power plants, solve it, and generate a clearing result for the virtual power plants. The clearing result includes the winning bid capacity and clearing price of the virtual power plants. The dispatch module is used to settle accounts with the virtual power plants based on the clearing price and the winning bid capacity, and to allocate the costs to green electricity users according to a preset green energy consumption ratio. At the same time, considering the maximum allocation fee that green electricity users are willing to pay for the green energy premium, the costs exceeding the green energy premium are diverted to green electricity companies according to a preset power generation ratio, thus allocating the adjustment costs generated during the operation of the virtual power plants.

[0015] Optionally, in one embodiment of this application, the objective function is:

[0016]

[0017] Where I represents the number of virtual power plants, and t represents the time point of the 96-point planned operation curve of the virtual power plants. Let be the charge / discharge state coefficient of the i-th virtual power plant at time t. Let i be the upward adjustment capacity reported in real time by the i-th virtual power plant at time t. Let be the adjusted price declared in real time by the i-th virtual power plant at time t. This represents the downward adjustment capacity reported in real time by the i-th virtual power plant at time t.

[0018] Optionally, in one embodiment of this application, the first calculation module includes: a first judgment unit, configured to, when the difference between the predicted output of new energy sources and the predicted demand of green electricity users on the operating day is greater than or equal to a preset threshold, the virtual power plant does not need to generate a pre-call plan; and a second judgment unit, configured to, when the difference between the predicted output of new energy sources and the predicted demand of green electricity users on the operating day is less than the preset threshold, organize the virtual power plant to conduct transactions while judging whether the flexible adjustable capacity of the aggregated resources of the virtual power plant meets preset conditions.

[0019] Optionally, in one embodiment of this application, the second determining unit is further configured to request off-site support from the dispatch center if the preset conditions are met; and to generate a pre-call plan for the virtual power plant if the preset conditions are not met.

[0020] Optionally, in one embodiment of this application, the construction module includes: a partitioning unit, used to partition the adjustable capacity according to the self-regulating capacity of the virtual power plant and the bidding method stipulated by the power trading center, to obtain the aggregated resource response allocation for each segment; and a prediction unit, used to confirm the data of the forecast of new energy output and the forecast of green electricity user demand on the operating day and to publish the prediction results and the upper limit of the bidding of the virtual power plant to each market participant.

[0021] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the green electricity short-term balance transaction scheduling method based on resource capacity equivalence as described in the above embodiments.

[0022] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described green electricity short-term balance transaction scheduling method based on resource capacity equivalence.

[0023] This application embodiment can determine the demand capacity based on relevant forecast information provided by the power dispatching department and the supply-demand deviation between new energy and power users, thereby determining the pre-dispatch capacity of virtual power plants. It also quantifies the flexible resources aggregated by virtual power plants, segmenting the adjustable capacity of virtual power plants, and constructing a bidding strategy for a 96-point planned operation curve and multi-segment quantity-price curve for the virtual power plant's daily operation. The power dispatching agency will construct an economic dispatch model for virtual power plants based on the quantity-price curves declared by the virtual power plants, with the objective function of minimizing operating costs. The model will then be solved to obtain the clearing result of the virtual power plants. Based on the winning bid capacity and clearing price of the virtual power plants, the revenue from participating in the short-term green balance service market will be calculated and allocated to green power users according to a preset green energy consumption ratio. Simultaneously, considering the maximum allocation fee that green power users are willing to pay for the green energy premium, any amount exceeding the green energy premium will be diverted to green power companies according to a preset power generation ratio. This allocation of adjustment costs incurred during the operation of the virtual power plants is beneficial for the efficient participation of virtual power plants in the short-term green balance service mechanism, improving the flexibility of resource adjustment capabilities, enhancing the elastic absorption margin of green power in the power grid, and improving the regional power supply-demand balance. This solves the technical problem in related technologies where regulation and optimization with power capacity as the control target is not conducive to mobilizing the enthusiasm of users to participate in resource allocation, making it difficult for virtual power plants to formulate capacity contract delivery targets based on the operating characteristics of different types of distributed resources and the attributes of user equipment utility.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 This is a flowchart of a short-term balancing transaction scheduling method for green electricity based on resource capacity equivalence, according to an embodiment of this application.

[0027] Figure 2 This is a flowchart of a short-term balance trading and scheduling method for green electricity based on resource capacity equivalence, according to one embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a green electricity short-term balancing transaction scheduling device based on resource capacity equivalence provided in an embodiment of this application;

[0029] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0031] The following describes, with reference to the accompanying drawings, a method and apparatus for short-term green electricity balance trading and scheduling based on resource capacity equivalence, according to embodiments of this application. Addressing the technical problem mentioned in the background section that regulation and optimization with power capacity as the control target is not conducive to mobilizing the enthusiasm of users to participate in resource allocation, making it difficult for virtual power plants to formulate capacity contract delivery targets based on the operating characteristics of different types of distributed resources and the attributes of user equipment utility, this application provides a method for short-term green electricity balance trading and scheduling based on resource capacity equivalence. In this method, based on relevant forecast information provided by the power dispatching department, the demand capacity can be determined by utilizing the supply-demand deviation between new energy and power users, thereby determining the pre-scheduled capacity of the virtual power plant, quantifying the flexible resources aggregated by the virtual power plant, realizing the segmentation of the adjustable capacity of the virtual power plant, and constructing a bidding strategy for a 96-point planned operating curve and a multi-segment quantity-price curve for the virtual power plant's operating days. The dispatching agency will construct an economic dispatch model for virtual power plants based on the quantity and price curves submitted by the virtual power plants, with the objective function of minimizing operating costs. This model will be solved to determine the clearing results of the virtual power plants. Based on the winning bid capacity and clearing price of the virtual power plants, the revenue from participating in the short-term green balance service market will be calculated and allocated to green electricity users according to a preset green energy consumption ratio. Simultaneously, considering the maximum green energy premium that green electricity users are willing to pay, any costs exceeding the green energy premium will be diverted to green electricity companies according to a preset generation ratio. This allocation of adjustment costs incurred during the operation of the virtual power plants facilitates their efficient participation in the short-term green balance service mechanism, improves flexible resource regulation capabilities, enhances the elastic absorption margin of green electricity in the power grid, and improves the regional power supply and demand balance. This solves the technical problem in related technologies where regulation optimization with power capacity as the control objective is not conducive to mobilizing the enthusiasm of users to participate in resource allocation, making it difficult for virtual power plants to formulate capacity contract delivery targets based on the operating characteristics of different types of distributed resources and the attributes of user equipment utility.

[0032] Specifically, Figure 1 This is a flowchart illustrating a short-term balancing transaction scheduling method for green electricity based on resource capacity equivalence, provided in an embodiment of this application.

[0033] like Figure 1 As shown, the green electricity short-term balancing transaction scheduling method based on resource capacity equivalence includes the following steps:

[0034] In step S101, based on the relevant forecast information provided by the power dispatching department, the demand capacity is determined by utilizing the supply-demand deviation between new energy sources and power users, and the pre-dispatch capacity of the virtual power plant is determined based on the demand capacity.

[0035] In actual implementation, the embodiments of this application can determine the demand capacity based on the supply and demand deviation of new energy and power users by using the relevant forecast information provided by the power dispatching department and the probability fitting of new energy and power users, and then determine the pre-dispatch capacity of the virtual power plant.

[0036] Specifically, the supply-demand imbalance can be expressed as the difference between renewable energy output and the energy demand of green electricity users. The predictive model for renewable energy output on operating days is as follows:

[0037]

[0038] Where D represents the number of new energy power stations. Let d be the power of the d-th new energy source.

[0039] The probability density model for the power output of renewable energy power plants can be:

[0040]

[0041] Where, λ d Let λ be the proportionality coefficient of the power output of the d-th renewable energy power station. d >0, k d Let k be the shape parameter of the power output of the d-th renewable energy power station. d >0, Let be the maximum power output of the d-th renewable energy power station. This represents the minimum power output of the d-th renewable energy power station.

[0042] The green electricity user demand forecasting model for the operating day can be:

[0043]

[0044] Where E represents the number of green electricity users. This represents the energy demand of the eth green electricity user.

[0045] The probability density model for the energy demand of green electricity users can be:

[0046]

[0047] in, Let σ be the medium- to long-term forecast of the energy demand of the e-th green electricity user. e Let be the unconstrained standard deviation of the energy demand of the e-th green electricity user. Let e ​​be the maximum energy demand of the e-th green electricity user. This represents the minimum energy demand of the e-th green electricity user.

[0048] The daily supply-demand deviation model for power system operation can be:

[0049]

[0050] Wherein, ΔP(t) represents the supply-demand deviation of the power system based on the forecast of renewable energy output and the forecast of energy demand from power users on the operating day. Let be the medium- to long-term forecast value of the average power of the d-th renewable energy power station. Γ is the gamma function.

[0051] Optionally, in one embodiment of this application, determining the pre-scheduled capacity of the virtual power plant based on demand capacity includes: when the difference between the predicted output of new energy sources and the predicted demand of green electricity users on the operating day is greater than or equal to a preset threshold, the virtual power plant does not need to generate a pre-scheduled plan; when the difference between the predicted output of new energy sources and the predicted demand of green electricity users on the operating day is less than the preset threshold, while organizing the virtual power plant to carry out transactions, it is determined whether the flexible adjustable capacity of the aggregated resources of the virtual power plant meets the preset conditions.

[0052] As one possible approach, the power trading center uses the difference ΔP between the forecast of renewable energy output and the forecast of green electricity user demand on the operating day to form a supply and demand balance supervision method and the virtual power plant pre-dispatch capacity on the operating day.

[0053] For example, when the preset threshold is 0, the specific regulatory method can be as follows:

[0054] 1) When ΔP≧0, the virtual power plant does not need to perform a pre-call plan;

[0055] 2) When ΔP<0, the power trading center organizes virtual power plants to conduct transactions, and at the same time needs to determine whether the flexible adjustable capacity of the aggregated virtual power plants meets the supply ΔP.

[0056] Optionally, in one embodiment of this application, after determining whether the adjustable capacity of the flexible resources aggregated by the virtual power plant meets the preset conditions, the method further includes: if the preset conditions are met, requesting off-site support from the dispatch center; if the preset conditions are not met, generating a pre-call plan for the virtual power plant.

[0057] Furthermore, the embodiments of this application can determine whether the flexible adjustable capacity of the aggregated virtual power plant meets preset conditions. When the preset conditions are met, the virtual power plant can form a pre-call plan; when the preset conditions are not met, the embodiments of this application need to request off-site support from the dispatching department, thereby realizing dispatching arrangements to cope with different situations.

[0058] In step S102, based on the pre-scheduled capacity of the virtual power plant, a bidding strategy is constructed for the planned operation curve of 96 points on the virtual power plant's operating day and the multi-segment quantity-price curve.

[0059] In actual implementation, the embodiments of this application can quantify the flexible resources aggregated by the virtual power plant, realize the segmentation of the adjustable capacity of the virtual power plant, and construct a bidding strategy for the 96-point planned operation curve and multi-segment quantity-price curve of the virtual power plant operation day.

[0060] Understandably, when virtual power plants participate in short-term green balance service transactions, they need to quantify the distributed resources they aggregate and form a segmented capacity that can be declared as a whole, according to the transaction rules. The distributed resources aggregated by virtual power plants include energy storage, thermal storage electric boilers, and electric vehicles, and the contract transaction volumes for different distributed resources participating in virtual power plant aggregation are not the same.

[0061] Energy storage is a flexible and distributed resource with both upward and downward adjustment capabilities. When participating in virtual power plant aggregation, distributed energy storage can use its own capacity control as the trading object and make real-time adjustments based on its own state of charge. Therefore, the quantitative model of virtual power plants aggregating distributed energy storage uses the energy storage contract capacity as the agreed quantity and makes real-time corrections based on the energy storage's own state of charge.

[0062] For example, in practical applications, electric vehicles, like distributed energy storage, have both uplink and downlink regulation capabilities. The difference lies in the individual differences of electric vehicles; their access time and charging duration are uncontrollable. Based on the randomness of electric vehicle behavior, virtual power plants, when aggregating electric vehicles, determine the contract transaction volume by agreeing on the state of charge of the electric vehicles when they leave the charging station, ensuring the normal travel of electric vehicles the following day.

[0063] Since thermal storage electric boilers only charge from virtual power plants and lack upward regulation capability (i.e., discharge capability), their upward regulation capacity is not considered in the virtual power plant's calculations; only their downward regulation capability (i.e., charging capability) is considered. Thermal storage electric boilers characterize their heating capacity by temperature. When participating in virtual power plant aggregation, the virtual power plant and the thermal storage electric boiler often use the agreed-upon temperature at a specified deadline as the contract transaction volume. The thermal storage electric boiler can guarantee heating demand for the following day at the agreed-upon temperature. The quantitative model for virtual power plant aggregation of distributed resources is as follows:

[0064]

[0065] in, Let be the maximum adjustable capacity of the i-th virtual power plant. Let θ be the maximum downward adjustable capacity of the i-th virtual power plant, A be the number of distributed energy storage units aggregated by the i-th virtual power plant, B be the number of thermal storage electric boilers aggregated by the i-th virtual power plant, C be the number of electric vehicles aggregated by the i-th virtual power plant, and θ be the maximum downward adjustable capacity of the i-th virtual power plant. a For the a-th distributed energy storage and virtual power plant, define the initial state of charge (SOC) value for the target time period. Let k be the contracted capacity size of the a-th distributed energy storage and virtual power plant. a Let be the charge / discharge coefficient of the a-th distributed energy storage, and be the 0-1 state coefficient. Let t be the minimum SOC value of the a-th distributed energy storage; b T is the starting temperature at which the b-th thermal storage electric boiler begins thermal storage. b For the b-th thermal storage electric boiler and the virtual power plant, the required temperature is agreed upon, W b S represents the heating heat index of the b-th thermal storage electric boiler. b T represents the heating area of ​​the b-th thermal storage electric boiler. h,b For the daily heating time of the b-th thermal storage electric boiler, η b Let k be the heat production efficiency of the b-th thermal storage electric boiler. c Let be the charge / discharge coefficient of the c-th electric vehicle, and be the 0-1 state coefficient; t c Let be the state of charge value of the c-th electric vehicle connected to the virtual power plant. Let δ be the minimum SOC value of the c-th electric vehicle. c Let E be the state of charge value agreed upon by the c-th electric vehicle and the virtual power plant when leaving the charging station. c Let be the battery capacity of the c-th electric vehicle.

[0066] The charge / discharge coefficient k of the a-th distributed energy storage a The charging and discharging coefficient k of the c-th electric vehicle c They can be represented as:

[0067]

[0068]

[0069] The adjustable capacity model of a virtual power plant can be:

[0070]

[0071] Among them, P i Let be the regulating capacity of the i-th virtual power plant that can balance the supply and demand imbalance in the power system.

[0072] In practice, virtual power plants can divide their adjustable capacity according to the pricing method stipulated by the power trading center, based on their own adjustable capacity, to form aggregated resource response allocation for each segment.

[0073] Optionally, in one embodiment of this application, a bidding strategy is constructed based on the pre-scheduled capacity of the virtual power plant to build a 96-point planned operation curve and a multi-segment quantity-price curve for the virtual power plant's operating day. This includes: dividing the adjustable capacity according to the pricing method stipulated by the power trading center based on the virtual power plant's self-regulating capacity, and obtaining the aggregated resource response allocation for each segment; confirming the data of the new energy output forecast and the green electricity user demand forecast for the operating day, and publishing the forecast results and the virtual power plant's price ceiling to each market participant.

[0074] In some embodiments, the bidding strategy for multiple virtual power plants is reflected in the specific application method for virtual power plants.

[0075] The power trading center can confirm the forecasts of renewable energy output and green electricity user demand on the operating day and promptly release the forecast results to various market participants, as well as the upper limit of virtual power plant bids.

[0076] Virtual power plants submit their planned 96-point daily operation curves and participation in the short-term green balancing service market's regulation capacity-regulation price curves to the power trading center. The price curves are submitted in a three-segment voluntary tiered manner, with each segment's declared capacity determined according to the proportion of adjustable capacity. For example, when the adjustable capacity ratio is 5:3:2, it can be as follows:

[0077]

[0078] Among them, t p T is the adjusted price declared in real time by virtual power plants. max R represents the upper limit of the unit capacity adjustment price for virtual power plants published by the power trading center, r represents the adjustment capacity declared by the virtual power plant in real time, and R represents the maximum capacity that the virtual power plant can adjust.

[0079] In step S103, based on the bidding strategy, according to the quantity and price curves declared by the virtual power plants, and with the minimization of operating costs as the objective function, an economic dispatch model for the virtual power plants is constructed and solved to form the clearing result of the virtual power plants. The clearing result includes the winning bid capacity and clearing price of the virtual power plants.

[0080] As one possible approach, in this embodiment of the application, the power dispatching agency can construct an economic dispatch model for virtual power plants based on the quantity and price curves declared by the virtual power plants, with the objective function of minimizing operating costs, and solve the model to form the clearing result of the virtual power plants, thereby realizing the economic dispatch of virtual power plants for short-term green balance services.

[0081] The clearing results include the winning bid capacity and clearing price of the virtual power plants.

[0082] Optionally, in one embodiment of this application, the objective function is:

[0083]

[0084] Where i represents the number of virtual power plants, and t represents the time point of the 96-point planned operation curve of the virtual power plants. Let be the charge / discharge state coefficient of the i-th virtual power plant at time t. Let i be the upward adjustment capacity reported in real time by the i-th virtual power plant at time t. Let be the adjusted price declared in real time by the i-th virtual power plant at time t. This represents the downward adjustment capacity reported in real time by the i-th virtual power plant at time t.

[0085] Specifically, power dispatching agencies can perform economic dispatching of virtual power plants based on the quantity and price curves submitted by the virtual power plants, with the objective function of minimizing operating costs. The objective function of the virtual power plant economic dispatching model can be:

[0086]

[0087] Where I represents the number of virtual power plants, and t represents the time point of the 96-point planned operation curve of the virtual power plants. Let be the charging / discharging state coefficient of the i-th virtual power plant at time t, and be the 0-1 state coefficient. Let i be the upward adjustment capacity reported in real time by the i-th virtual power plant at time t. Let be the adjusted price declared in real time by the i-th virtual power plant at time t. This represents the downward adjustment capacity reported in real time by the i-th virtual power plant at time t.

[0088] in, and All satisfy the virtual power plant bidding strategy, that is, satisfy the relevant constraints in step S102.

[0089] The charge / discharge state coefficient of the i-th virtual power plant at time t It can be represented as:

[0090]

[0091] The constraints on the winning bid capacity of virtual power plants can be:

[0092] P1+P i +…+P I <<ΔP i∈[1,I],

[0093] Where ΔP is the power system supply-demand deviation, P i Let be the winning bid capacity of the i-th virtual power plant.

[0094] In step S104, the virtual power plant is settled according to the clearing price and the winning bid capacity, and the cost is allocated to green electricity users according to the preset green energy consumption ratio. At the same time, the maximum cost that green electricity users are willing to pay for the green energy premium is considered, and the cost exceeding the green energy premium is diverted to green electricity companies according to the preset power generation ratio to share the adjustment costs generated during the operation of the virtual power plant.

[0095] In practice, green electricity users enjoy the supply of green electricity and are willing to pay their share of the green energy premium. They also compare the green energy premium with the price of traditional thermal power to determine the maximum share they are willing to pay. The maximum share of the green energy premium that green electricity users are willing to pay can be:

[0096]

[0097] in, Q represents the maximum share of the green energy premium that the nth green electricity user is willing to pay. n For the green energy consumption of the nth green electricity user, P T P represents the traditional average electricity price for thermal power plants. G This represents the average selling price of green electricity.

[0098] The virtual power plant regulation cost can be reasonably allocated based on the highest share of the total environmental premium paid by green electricity users, according to different circumstances, as follows:

[0099] 1) When At that time, the virtual power plant regulation costs will be shared by all green electricity users, and the sharing method will be as follows.

[0100] Green electricity users benefit from the green electricity supply, saving on electricity costs and improving social benefits. They become the actual beneficiaries of the green supply and demand balance regulation service. Based on the principle of "whoever benefits, bears the cost," the virtual power plant regulation costs are allocated to green electricity users according to the proportion of green energy consumption.

[0101] The total adjustment costs for a virtual power plant can be:

[0102]

[0103] Among them, H i Let i be the revenue of the i-th virtual power plant.

[0104] The cost shared by each green electricity user can be:

[0105]

[0106] Among them, H n The cost shared by the nth green electricity user, Q n Let N represent the green energy consumption of the nth green electricity user, where N is the number of green electricity users.

[0107] 2) When At that time, the energy cost for green electricity users using traditional thermal power will be lower than that of green electricity, and green electricity users will no longer be willing to continue to share costs exceeding those of using traditional thermal power. The regulation costs of the virtual power plant will be shared by green electricity users and green electricity companies, and the sharing method will be calculated as follows.

[0108] The cost shared by green electricity users is as follows:

[0109]

[0110] The total cost allocated to green energy companies is:

[0111]

[0112] Among them, H T The total cost allocated to green energy companies.

[0113] Green energy companies allocate costs according to the proportion of their electricity generation. Specific allocation methods may include:

[0114]

[0115] in, G is the cost allocated to the m-th green energy company. m Let M be the power generation of the m-th green energy company, and M be the number of green energy companies participating in the transaction.

[0116] Combination Figure 2 As shown, the working principle of the green electricity short-term balance transaction scheduling method based on resource capacity equivalence in this application is explained in detail.

[0117] like Figure 2 As shown, embodiments of this application may include the following steps:

[0118] Step S201: Regulatory method for short-term green supply and demand balance. In actual implementation, the embodiments of this application can determine the demand capacity based on the supply and demand deviation of new energy and power users by using the relevant forecast information provided by the power dispatching department and probability fitting, and then determine the pre-dispatch capacity of virtual power plants.

[0119] Specifically, the supply-demand imbalance can be expressed as the difference between renewable energy output and the energy demand of green electricity users. The predictive model for renewable energy output on operating days is as follows:

[0120]

[0121] Where D represents the number of new energy power stations. Let d be the power of the d-th new energy source.

[0122] The probability density model for the power output of renewable energy power plants can be:

[0123]

[0124] Where, λ d Let λ be the proportionality coefficient of the power output of the d-th renewable energy power station. d >0, k d Let k be the shape parameter of the power output of the d-th renewable energy power station. d >0, Let be the maximum power output of the d-th renewable energy power station. This represents the minimum power output of the d-th renewable energy power station.

[0125] The green electricity user demand forecasting model for the operating day can be:

[0126]

[0127] Where E represents the number of green electricity users. This represents the energy demand of the eth green electricity user.

[0128] The probability density model for the energy demand of green electricity users can be:

[0129]

[0130] in, Let σ be the medium- to long-term forecast of the energy demand of the e-th green electricity user. e Let be the unconstrained standard deviation of the energy demand of the e-th green electricity user. Let e ​​be the maximum energy demand of the e-th green electricity user. This represents the minimum energy demand of the e-th green electricity user.

[0131] The daily supply-demand deviation model for power system operation can be:

[0132]

[0133] Wherein, ΔP(t) represents the supply-demand deviation of the power system based on the forecast of renewable energy output and the forecast of energy demand from power users on the operating day. Let be the medium- to long-term forecast value of the average power of the d-th renewable energy power station. Γ is the gamma function.

[0134] As one possible approach, the power trading center uses the difference ΔP between the forecast of renewable energy output and the forecast of green electricity user demand on the operating day to form a supply and demand balance supervision method and the virtual power plant pre-dispatch capacity on the operating day.

[0135] For example, when the preset threshold is 0, the specific regulatory method can be as follows:

[0136] 1) When ΔP≧0, the virtual power plant does not need to perform a pre-call plan;

[0137] 2) When ΔP<0, the power trading center organizes virtual power plants to conduct transactions, and at the same time needs to determine whether the flexible adjustable capacity of the aggregated virtual power plants meets the supply ΔP.

[0138] Furthermore, the embodiments of this application can determine whether the flexible adjustable capacity of the aggregated virtual power plant meets preset conditions. When the preset conditions are met, the virtual power plant can form a pre-call plan; when the preset conditions are not met, the embodiments of this application need to request off-site support from the dispatching department, thereby realizing dispatching arrangements to cope with different situations.

[0139] Step S202: Equivalent Quantification of the Adjustable Range of the Virtual Power Plant. It is understood that when a virtual power plant participates in short-term green balancing service transactions, it needs to quantify the distributed resources it aggregates, forming a segmented capacity that can be declared as a whole according to the transaction rules. The distributed resources aggregated by the virtual power plant include energy storage, thermal storage electric boilers, and electric vehicles. The contract transaction volumes for different distributed resources participating in the virtual power plant aggregation are not entirely the same.

[0140] Energy storage is a flexible and distributed resource with both upward and downward adjustment capabilities. When participating in virtual power plant aggregation, distributed energy storage can use its own capacity control as the trading object and make real-time adjustments based on its own state of charge. Therefore, the quantitative model of virtual power plants aggregating distributed energy storage uses the energy storage contract capacity as the agreed quantity and makes real-time corrections based on the energy storage's own state of charge.

[0141] For example, in practical applications, electric vehicles, like distributed energy storage, have both uplink and downlink regulation capabilities. The difference lies in the individual differences of electric vehicles; their access time and charging duration are uncontrollable. Based on the randomness of electric vehicle behavior, virtual power plants, when aggregating electric vehicles, determine the contract transaction volume by agreeing on the state of charge of the electric vehicles when they leave the charging station, ensuring the normal travel of electric vehicles the following day.

[0142] Since thermal storage electric boilers only charge from virtual power plants and lack upward regulation capability (i.e., discharge capability), their upward regulation capacity is not considered in the virtual power plant's calculations; only their downward regulation capability (i.e., charging capability) is considered. Thermal storage electric boilers characterize their heating capacity by temperature. When participating in virtual power plant aggregation, the virtual power plant and the thermal storage electric boiler often use the agreed-upon temperature at a specified deadline as the contract transaction volume. The thermal storage electric boiler can guarantee heating demand for the following day at the agreed-upon temperature. The quantitative model for virtual power plant aggregation of distributed resources is as follows:

[0143]

[0144] in, Let be the maximum adjustable capacity of the i-th virtual power plant. Let θ be the maximum downward adjustable capacity of the i-th virtual power plant, A be the number of distributed energy storage units aggregated by the i-th virtual power plant, B be the number of thermal storage electric boilers aggregated by the i-th virtual power plant, C be the number of electric vehicles aggregated by the i-th virtual power plant, and θ be the maximum downward adjustable capacity of the i-th virtual power plant. a For the a-th distributed energy storage and virtual power plant, define the initial state of charge (SOC) value for the target time period. Let k be the contracted capacity size of the a-th distributed energy storage and virtual power plant. a Let be the charge / discharge coefficient of the a-th distributed energy storage, and be the 0-1 state coefficient. Let t be the minimum SOC value of the a-th distributed energy storage; b T is the starting temperature at which the b-th thermal storage electric boiler begins thermal storage. b For the b-th thermal storage electric boiler and the virtual power plant, the required temperature is agreed upon, W b S represents the heating heat index of the b-th thermal storage electric boiler. b T represents the heating area of ​​the b-th thermal storage electric boiler. h,b For the daily heating time of the b-th thermal storage electric boiler, η b Let k be the heat production efficiency of the b-th thermal storage electric boiler. c Let be the charge / discharge coefficient of the c-th electric vehicle, and be the 0-1 state coefficient; t c Let be the state of charge value of the c-th electric vehicle connected to the virtual power plant. Let δ be the minimum SOC value of the c-th electric vehicle. c Let E be the state of charge value agreed upon by the c-th electric vehicle and the virtual power plant when leaving the charging station. c Let be the battery capacity of the c-th electric vehicle.

[0145] The charge / discharge coefficient k of the a-th distributed energy storage a The charging and discharging coefficient k of the c-th electric vehicle c They can be represented as:

[0146]

[0147]

[0148] The adjustable capacity model of a virtual power plant can be:

[0149]

[0150] Among them, P i Let be the regulating capacity of the i-th virtual power plant that can balance the supply and demand imbalance in the power system.

[0151] In practice, virtual power plants can divide their adjustable capacity according to the pricing method stipulated by the power trading center, based on their own adjustable capacity, to form aggregated resource response allocation for each segment.

[0152] Step S203: Tiered bidding model for multiple virtual power plants. In some embodiments, the bidding strategy for multiple virtual power plants is reflected in the specific application method of the virtual power plants.

[0153] The power trading center can confirm the forecasts of renewable energy output and green electricity user demand on the operating day and promptly release the forecast results to various market participants, as well as the upper limit of virtual power plant bids.

[0154] Virtual power plants submit their planned 96-point daily operation curves and participation in the short-term green balancing service market's regulation capacity-regulation price curves to the power trading center. The price curves are submitted in a three-segment voluntary tiered manner, with each segment's declared capacity determined according to the proportion of adjustable capacity. For example, when the adjustable capacity ratio is 5:3:2, it can be as follows:

[0155]

[0156] Among them, t p T is the adjusted price declared in real time by virtual power plants. max R represents the upper limit of the unit capacity adjustment price for virtual power plants published by the power trading center, r represents the adjustment capacity declared by the virtual power plant in real time, and R represents the maximum capacity that the virtual power plant can adjust.

[0157] Step S204: Economic Dispatch Model for Virtual Power Plants in Short-Term Green Balancing Services. Specifically, the power dispatching agency can perform economic dispatch on virtual power plants based on the quantity and price curves declared by the virtual power plants, with the objective function of minimizing operating costs. The objective function of the virtual power plant economic dispatch model can be:

[0158]

[0159] Where I represents the number of virtual power plants, and t represents the time point of the 96-point planned operation curve of the virtual power plants. Let be the charging / discharging state coefficient of the i-th virtual power plant at time t, and be the 0-1 state coefficient. Let i be the upward adjustment capacity reported in real time by the i-th virtual power plant at time t. Let be the adjusted price declared in real time by the i-th virtual power plant at time t. This represents the downward adjustment capacity reported in real time by the i-th virtual power plant at time t.

[0160] in, and All of them satisfy the virtual power plant bidding strategy, that is, they satisfy the relevant constraints in the above steps.

[0161] The charge / discharge state coefficient of the i-th virtual power plant at time t It can be represented as:

[0162]

[0163] The constraints on the winning bid capacity of virtual power plants can be:

[0164] P1+P i +…+P I <<ΔP i∈[1,I],

[0165] Where ΔP is the power system supply-demand deviation, P i Let be the winning bid capacity of the i-th virtual power plant.

[0166] Step S205: Environmental Premium Assessment Method for Green Electricity on a Short-Term Scale. In practice, green electricity users enjoy the supply of green electricity and are willing to pay their share of the green energy premium. They also compare the green energy premium with the price of traditional thermal power to determine the maximum share. The maximum share of the green energy premium that green electricity users are willing to pay can be:

[0167]

[0168] in, Q represents the maximum share of the green energy premium that the nth green electricity user is willing to pay. n For the green energy consumption of the nth green electricity user, P T P represents the traditional average electricity price for thermal power plants. G This represents the average selling price of green electricity.

[0169] Step S206: Consider the system balancing cost allocation method for environmental premiums. The virtual power plant regulation costs can be reasonably allocated based on the highest allocation cost within the total environmental premium for green electricity users, according to different scenarios, as follows:

[0170] 1) When At that time, the virtual power plant regulation costs will be shared by all green electricity users, and the sharing method will be as follows.

[0171] Green electricity users benefit from the green electricity supply, saving on electricity costs and improving social benefits. They become the actual beneficiaries of the green supply and demand balance regulation service. Based on the principle of "whoever benefits, bears the cost," the virtual power plant regulation costs are allocated to green electricity users according to the proportion of green energy consumption.

[0172] The total adjustment costs for a virtual power plant can be:

[0173]

[0174] Among them, H i Let i be the revenue of the i-th virtual power plant.

[0175] The cost shared by each green electricity user can be:

[0176]

[0177] Among them, H n The cost shared by the nth green electricity user, Q n Let N represent the green energy consumption of the nth green electricity user, where N is the number of green electricity users.

[0178] 3) When At that time, the energy cost for green electricity users using traditional thermal power will be lower than that of green electricity, and green electricity users will no longer be willing to continue to share costs exceeding those of using traditional thermal power. The regulation costs of the virtual power plant will be shared by green electricity users and green electricity companies, and the sharing method will be calculated as follows.

[0179] The cost shared by green electricity users is as follows:

[0180]

[0181] The total cost allocated to green energy companies is:

[0182]

[0183] Among them, H T The total cost allocated to green energy companies.

[0184] Green energy companies allocate costs according to the proportion of their electricity generation. Specific allocation methods may include:

[0185]

[0186] in, G is the cost allocated to the m-th green energy company. m Let M be the power generation of the m-th green energy company, and M be the number of green energy companies participating in the transaction.

[0187] The green electricity short-term balance trading and dispatching method based on resource capacity equivalence proposed in this application can determine the demand capacity based on relevant forecast information provided by the power dispatching department and the supply-demand deviation between new energy and power users, thereby determining the pre-dispatch capacity of virtual power plants. It quantifies the flexible resources aggregated by virtual power plants, realizes the segmentation of adjustable capacity of virtual power plants, and constructs a bidding strategy for a 96-point planned operation curve and multi-segment quantity-price curve for the virtual power plant's daily operation. The power dispatching agency will construct an economic dispatching model for virtual power plants based on the quantity-price curves declared by the virtual power plants, with the goal of minimizing operating costs, and then solve the model. The clearing results of virtual power plants are used to calculate the revenue of virtual power plants participating in the short-term green balance service market based on their winning bid capacity and clearing price. This revenue is then distributed to green electricity users according to a preset green energy consumption ratio. Simultaneously, considering the maximum green energy premium that green electricity users are willing to pay, any costs exceeding the green energy premium are diverted to green electricity companies according to a preset generation ratio. This also helps to distribute the adjustment costs incurred during the operation of the virtual power plants. This approach facilitates the efficient participation of virtual power plants in the short-term green balance service mechanism, improves the flexibility of resource regulation, enhances the elastic absorption margin of green electricity in the power grid, and improves the balance of power supply and demand within the region. This solves the technical problem in related technologies where regulation optimization based on power capacity as the control target is not conducive to mobilizing the enthusiasm of users to participate in resource allocation, making it difficult for virtual power plants to formulate capacity contract delivery targets based on the operating characteristics of different types of distributed resources and the attributes of user equipment utility.

[0188] Next, referring to the accompanying drawings, a green electricity short-term balancing transaction scheduling device based on resource capacity equivalence, according to an embodiment of this application, is described.

[0189] Figure 3 This is a block diagram of a green electricity short-term balance trading scheduling device based on resource capacity equivalence, according to an embodiment of this application.

[0190] like Figure 3 As shown, the green electricity short-term balance transaction scheduling device 10 based on resource capacity equivalence includes: a first calculation module 100, a construction module 200, a second calculation module 300, and a scheduling module 400.

[0191] Specifically, the first calculation module 100 is used to determine the demand capacity based on the relevant forecast information provided by the power dispatching department, by utilizing the supply and demand deviation between new energy sources and power users, and to determine the pre-dispatch capacity of the virtual power plant based on the demand capacity.

[0192] Module 200 is used to construct a bidding strategy for the planned operation curve and multi-segment quantity-price curve of the virtual power plant based on the pre-scheduled capacity of the virtual power plant.

[0193] The second calculation module 300 is used to construct an economic dispatch model for virtual power plants based on the bidding strategy, according to the quantity and price curves declared by the virtual power plants, with the objective function of minimizing operating costs, and to solve the model to form the clearing result of the virtual power plants. The clearing result includes the winning bid capacity and clearing price of the virtual power plants.

[0194] The dispatch module 400 is used to settle accounts with virtual power plants based on clearing prices and winning bid capacity, and to allocate the costs to green electricity users according to the preset green energy consumption ratio. At the same time, it takes into account the maximum allocation fee that green electricity users are willing to pay for the green energy premium, and diverts the cost exceeding the green energy premium to green electricity companies according to the preset power generation ratio, thus allocating the adjustment costs generated during the operation of the virtual power plant.

[0195] Optionally, in one embodiment of this application, the objective function is:

[0196]

[0197] Where I represents the number of virtual power plants, and t represents the time point of the 96-point planned operation curve of the virtual power plants. Let be the charge / discharge state coefficient of the i-th virtual power plant at time t. Let i be the upward adjustment capacity reported in real time by the i-th virtual power plant at time t. Let be the adjusted price declared in real time by the i-th virtual power plant at time t. This represents the downward adjustment capacity reported in real time by the i-th virtual power plant at time t.

[0198] Optionally, in one embodiment of this application, the first calculation module 100 includes: a first judgment unit and a second judgment unit.

[0199] The first judgment unit is used so that when the difference between the predicted output of new energy on the operating day and the predicted demand of green electricity users on the operating day is greater than or equal to a preset threshold, the virtual power plant does not need to generate a pre-call plan.

[0200] The second judgment unit is used to determine whether the adjustable capacity of the flexible resources aggregated by the virtual power plant meets the preset conditions when the difference between the predicted output of new energy on the operating day and the predicted demand of green electricity users on the operating day is less than a preset threshold.

[0201] Optionally, in one embodiment of this application, the second judgment unit is further configured to request off-site support from the dispatch center if the preset conditions are met; and to generate a pre-call plan for the virtual power plant if the preset conditions are not met.

[0202] Optionally, in one embodiment of this application, the construction module 200 includes: a partitioning unit and a prediction unit.

[0203] The division unit is used to divide the adjustable capacity according to the pricing method stipulated by the power trading center based on the virtual power plant's own adjustment capacity, and to obtain the aggregated resource response allocation for each segment.

[0204] The forecasting unit is used to confirm the forecasts of renewable energy output and green electricity user demand on the operating day, and to publish the forecast results and the upper limit of virtual power plant bids to various market participants.

[0205] It should be noted that the foregoing explanation of the green electricity short-term balance transaction scheduling method based on resource capacity equivalence also applies to the green electricity short-term balance transaction scheduling device based on resource capacity equivalence in this embodiment, and will not be repeated here.

[0206] According to the embodiments of this application, the green electricity short-term balance trading and dispatching device based on resource capacity equivalence can determine the demand capacity based on relevant forecast information provided by the power dispatching department and the supply-demand deviation between new energy and power users, thereby determining the pre-dispatch capacity of virtual power plants, quantifying the flexible resources aggregated by virtual power plants, realizing the segmentation of the adjustable capacity of virtual power plants, constructing a bidding strategy for a 96-point planned operation curve and multi-segment quantity-price curve for the virtual power plant's operating days, and the power dispatching agency will construct an economic dispatching model for virtual power plants based on the quantity-price curves declared by the virtual power plants, with the goal of minimizing operating costs, and then solve the model. The clearing results of virtual power plants are used to calculate the revenue of virtual power plants participating in the short-term green balance service market based on their winning bid capacity and clearing price. This revenue is then distributed to green electricity users according to a preset green energy consumption ratio. Simultaneously, considering the maximum green energy premium that green electricity users are willing to pay, any costs exceeding the green energy premium are diverted to green electricity companies according to a preset generation ratio. This also helps to distribute the adjustment costs incurred during the operation of the virtual power plants. This approach facilitates the efficient participation of virtual power plants in the short-term green balance service mechanism, improves the flexibility of resource regulation, enhances the elastic absorption margin of green electricity in the power grid, and improves the balance of power supply and demand within the region. This solves the technical problem in related technologies where regulation optimization based on power capacity as the control target is not conducive to mobilizing the enthusiasm of users to participate in resource allocation, making it difficult for virtual power plants to formulate capacity contract delivery targets based on the operating characteristics of different types of distributed resources and the attributes of user equipment utility.

[0207] Figure 4A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0208] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0209] When the processor 402 executes the program, it implements the green electricity short-term balance transaction scheduling method based on resource capacity equivalence provided in the above embodiments.

[0210] Furthermore, electronic devices also include:

[0211] Communication interface 403 is used for communication between memory 401 and processor 402.

[0212] The memory 401 is used to store computer programs that can run on the processor 402.

[0213] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0214] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0215] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0216] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0217] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described green electricity short-term balance transaction scheduling method based on resource capacity equivalence.

[0218] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0219] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0220] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0221] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0222] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0223] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0224] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0225] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for short-term balancing trading and scheduling of green electricity based on resource capacity equivalence, characterized in that, Includes the following steps: Based on the relevant forecast information provided by the power dispatching department, the demand capacity is determined by utilizing the supply and demand deviation between new energy sources and power users, and the pre-dispatch capacity of the virtual power plant is determined according to the demand capacity. Wherein, when the difference between the forecast of new energy output on the operating day and the forecast of green power user demand on the operating day is greater than or equal to a preset threshold, the virtual power plant does not need to generate a pre-dispatch plan. When the difference between the predicted output of new energy on the operating day and the predicted demand of green electricity users on the operating day is less than the preset threshold, the virtual power plant is organized to carry out transactions, and it is determined whether the flexible resource adjustable capacity aggregated by the virtual power plant meets the preset conditions. The quantitative model for aggregating distributed resources in virtual power plants has the ability to adjust capacity both upwards and downwards. Based on the pre-scheduled capacity of the virtual power plant, a bidding strategy is constructed for the planned operation curve of 96 points on the virtual power plant's operating day and the multi-segment quantity-price curve. Based on the bidding strategy, according to the quantity and price curves declared by the virtual power plants, an economic dispatch model for virtual power plants is constructed with the goal of minimizing operating costs, and the model is solved to form the clearing result of the virtual power plants. The clearing result includes the winning bid capacity and clearing price of the virtual power plants. The virtual power plant is settled based on the clearing price and the winning bid capacity, and the costs are allocated to green electricity users according to the preset green energy consumption ratio. At the same time, the maximum allocation fee that green electricity users are willing to pay for the green energy premium is taken into account, and the cost exceeding the green energy premium is diverted to green electricity companies according to the preset power generation ratio to share the adjustment costs generated during the operation of the virtual power plant.

2. The method according to claim 1, characterized in that, The objective function is: ， in, The number of virtual power plants. The time points for the 96-point planned operation curve of the virtual power plant. For the first The virtual power plant in the first The state of charge / discharge coefficient at a given moment. For the first The virtual power plant in the first The upward adjustment capacity is reported in real time at each moment. For the first The virtual power plant in the first Adjusted prices are reported in real time at each moment. For the first The virtual power plant in the first The downward adjustment capacity is reported in real time at each moment.

3. The method according to claim 1, characterized in that, After determining whether the flexible adjustable capacity of the aggregated virtual power plant meets the preset conditions, the method further includes: If the preset conditions are met, request off-site support from the dispatcher; If the preset conditions are not met, a pre-call plan for the virtual power plant is generated.

4. The method according to claim 1, characterized in that, The bidding strategy, which constructs a 96-point planned operation curve and a multi-segment quantity-price curve for the virtual power plant based on its pre-scheduled capacity, includes: Based on the self-regulating capacity of the virtual power plant, the adjustable capacity is divided according to the bidding method stipulated by the power trading center, and the aggregated resource response allocation for each segment is obtained. The forecasts for renewable energy output and green electricity demand on the operating day are confirmed, and the forecast results and the upper limit of the price quoted by the virtual power plant are released to various market participants.

5. A short-term balancing trading and scheduling device for green electricity based on resource capacity equivalence, characterized in that, include: The first calculation module is used to determine the demand capacity based on relevant forecast information provided by the power dispatching department, utilizing the supply-demand deviation between new energy sources and power users, and to determine the pre-dispatch capacity of the virtual power plant based on the demand capacity, wherein it includes: The first judgment unit is used so that when the difference between the predicted output of new energy on the operating day and the predicted demand of green electricity users on the operating day is greater than or equal to a preset threshold, the virtual power plant does not need to generate a pre-call plan. The second judgment unit is used to determine whether the flexible resource adjustable capacity aggregated by the virtual power plant meets the preset conditions when the difference between the predicted output of new energy on the operating day and the predicted demand of green electricity users on the operating day is less than the preset threshold. The quantitative model for aggregating distributed resources in virtual power plants has the ability to adjust capacity both upwards and downwards. The module is used to construct a bidding strategy for the virtual power plant's daily 96-point planned operation curve and multi-segment quantity-price curve based on the virtual power plant's pre-scheduled capacity. The second calculation module is used to construct an economic dispatch model for virtual power plants based on the bidding strategy, according to the quantity and price curves declared by the virtual power plants, with the goal of minimizing operating costs, and solve the model to form the clearing result of the virtual power plants. The clearing result includes the winning bid capacity and clearing price of the virtual power plants. The scheduling module is used to settle accounts with the virtual power plant based on the clearing price and the winning bid capacity, and to allocate the costs to green electricity users according to the preset green energy consumption ratio. At the same time, it takes into account the maximum allocation fee that green electricity users are willing to pay for the green energy premium, and diverts the cost exceeding the green energy premium to green electricity companies according to the preset power generation ratio, thereby allocating the adjustment costs generated during the operation of the virtual power plant.

6. The apparatus according to claim 5, characterized in that, The objective function is: ， in, The number of virtual power plants. The time points for the 96-point planned operation curve of the virtual power plant. For the first The virtual power plant in the first The state of charge / discharge coefficient at a given moment. For the first The virtual power plant in the first The upward adjustment capacity is reported in real time at each moment. For the first The virtual power plant in the first Adjusted prices are reported in real time at each moment. For the first The virtual power plant in the first The downward adjustment capacity is reported in real time at each moment.

7. The apparatus according to claim 5, characterized in that, The second judgment unit is further configured to request off-site support from the dispatch center if the preset conditions are met; otherwise, generate a pre-call plan for the virtual power plant.

8. The apparatus according to claim 5, characterized in that, The building module includes: The partitioning unit is used to partition the adjustable capacity according to the pricing method stipulated by the power trading center based on the self-regulating capacity of the virtual power plant, and obtain the aggregated resource response allocation for each segment; The forecasting unit is used to confirm the forecasts of renewable energy output and green electricity user demand on the operating day, and to publish the forecast results and the upper limit of the price quoted by the virtual power plant to various market participants.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the green electricity short-term balance trading scheduling method based on resource capacity equivalence as described in any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the green electricity short-term balance transaction scheduling method based on resource capacity equivalence as described in any one of claims 1-4.

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