A wind-solar power station deviation processing method based on shared energy storage and related device

Abstract

This invention belongs to the field of new energy power generation and relates to a method and related device for handling deviations in wind and solar power plants based on shared energy storage. The wind-solar-storage cluster achieves mutual deviation protection with the shared energy storage through charging and discharging strategies. Aiming to maximize the revenue of the shared energy storage operator, a pricing model for mutual deviation protection is constructed, and the operator's pricing is obtained. The day-ahead spot price is determined based on the shared energy storage operator's pricing. The decomposition of the medium- and long-term electricity from the wind and solar power plants to the daily level is compared with the day-ahead spot price, and the deviation handling strategy for the wind and solar power plants is determined based on the comparison results. This scheme introduces shared energy storage, fully utilizing the output characteristics and complementary processing capabilities of the new energy power plants to achieve deviation complementarity with shared energy storage, and optimizing the control of both the new energy power plants and shared energy storage. It can coordinate the output and energy storage resources of each new energy power plant, leverage the synergistic complementarity between the wind and solar power plants and shared energy storage, and maximize the revenue of both the wind-solar power plant cluster and the shared energy storage.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation, specifically relating to a method and related device for handling deviations in wind and solar power plants based on shared energy storage. Background Technology

[0002] New energy has entered a new stage of high-quality leapfrog development, with wind and solar power exhibiting characteristics such as large scale, high proportion, and marketization. The randomness of both supply and demand has profoundly impacted the operation of traditional power systems and the construction of the electricity market, leading to an increasing demand for shared energy storage. While wind and solar power are considered sustainable and environmentally friendly energy sources, from a system perspective, the uncertainty and limited predictability of wind and solar power necessitate the deployment of more reserve power to reduce wind curtailment or power restrictions, especially given insufficient flexibility. Therefore, the large-scale utilization of renewable energy sources with random fluctuations in output presents numerous challenges to power system dispatch and operation, increasing system operating costs; on the other hand, it results in significant energy waste and reduces the cost-effectiveness of renewable energy. Summary of the Invention

[0003] The purpose of this invention is to provide a method and related apparatus for handling deviations in wind and solar power plants based on shared energy storage, so as to solve the problem of reducing the cost-effectiveness of renewable energy in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a method for handling deviations in wind and solar power plants based on shared energy storage, comprising the following steps:

[0006] The composition of the wind-solar-storage cluster is determined, including several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system.

[0007] Based on wind-solar-storage clusters and deviation mutual guarantees, and with the goal of maximizing the revenue of shared energy storage operators, a deviation mutual guarantee pricing model for shared energy storage operators is constructed, and the constraints of the shared energy storage operator deviation mutual guarantee pricing model are determined. Based on the constraints, the shared energy storage operator deviation mutual guarantee pricing model is solved to obtain the shared energy storage operator pricing, and the day-ahead spot price is determined based on the shared energy storage operator pricing.

[0008] Obtain relevant historical data of wind and solar power stations, decompose the medium- and long-term power generation of wind and solar power stations to the daily level based on the historical data, and obtain the daily decomposition results; compare the daily decomposition results with the day-ahead spot market, and determine the deviation handling strategy for wind and solar power stations based on the comparison results.

[0009] Furthermore, based on wind-solar-storage clusters and deviation mutual guarantees, and with the goal of maximizing the revenue of shared energy storage operators, a deviation mutual guarantee pricing model for shared energy storage operators is constructed, wherein:

[0010] The shared energy storage operator deviation mutual guarantee pricing model is as follows:

[0011]

[0012] Where R represents the revenue of shared energy storage operators, A represents the deviation insurance pricing standard for installed capacity of multiple market participants, G represents the installed capacity of multiple market participants purchasing deviation insurance services, and C represents the installed capacity of multiple market participants purchasing deviation insurance services. LCOS denoted as the annual levelized unit cost of energy storage; x represents the installed capacity of the energy storage power station; F(x) represents the annual deviation assessment cost when the installed capacity of energy storage is x.

[0013] Furthermore, the installed capacity G of multiple market participants purchasing deviation insurance services, and the annual standardized unit cost C of energy storage. LCOS The annual deviation assessment cost F(x) for an energy storage installed capacity of x is expressed as follows:

[0014]

[0015] Where q represents the number of market participants purchasing deviation insurance services; l represents the starting day; G 0 j C represents the installed capacity of the j-th market entity represented by the shared energy storage operator; V C represents the investment cost per unit of energy storage capacity. M The operating cost per unit of energy storage capacity; C H Maintenance cost per unit energy storage capacity; C E denoted as the decommissioning cost per unit of energy storage capacity; r represents the expected financial internal rate of return for shared energy storage operators; n represents the operating life of the energy storage power station; f(x) represents the typical daily deviation assessment cost when the installed energy storage capacity is x; m represents the total number of days in the assessment period; D θ It indicates the number of days in each of the four seasons of spring, summer, autumn, and winter within a year.

[0016] Furthermore, the constraints of the shared energy storage operator deviation mutual guarantee pricing model are determined, including:

[0017] The constraints include: insurance cost constraints and shared energy storage power station charging and discharging constraints; the shared energy storage power station charging and discharging constraints include instantaneous charging and discharging power constraints, energy storage installed capacity constraints, and charging and discharging conversion efficiency constraints.

[0018] Furthermore, insurance cost constraints include:

[0019]

[0020] Where A represents the deviation insurance pricing standard (yuan / MW·year) for installed capacity of multiple market entities; G 0 j Let F represent the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator; and the deviation assessment fee F of the j-th market entity in one year. 0 Yj C LCOS This represents the average annual cost per unit of energy storage (RMB / MW·year); x j The energy storage capacity built for the j-th market entity.

[0021] Furthermore, relevant historical data of wind and solar power stations are obtained, and the medium- and long-term electricity generation of wind and solar power stations is decomposed into daily amounts based on the historical data, resulting in daily decomposition results, including:

[0022] The proportion of theoretical renewable energy power and load power for each month is determined based on the medium- and long-term power generation of wind and solar power stations.

[0023] Using the average of the theoretical electricity share and load electricity share of new energy in each month as the allocation ratio, the annual trading volume of new energy is roughly divided to obtain the initial value of the optimized trading volume of new energy in each month, and is updated monthly based on the implementation status. If part of the annual trading volume has been completed from month 1 to month D-1, then the initial value of the monthly trading volume of new energy in month D is allocated as follows:

[0024]

[0025] Based on the monthly transaction volume breakdown, daily volume proportions are set, with weekday proportion α and Saturday / Sunday proportion β. The daily contract volume breakdown is as follows:

[0026] Breakdown of weekday electricity consumption:

[0027] Weekend daily electricity consumption breakdown:

[0028] In the formula: a NE (D) represents the proportion of the theoretical renewable energy power generation in month D to the sum of the theoretical renewable energy power generation from month D to month 12; Q NE* (d) represents the theoretical renewable energy power generation for month d, which can be obtained by accumulating the theoretical renewable energy power value over time periods; a L (D) represents the proportion of the electricity load in month D to the sum of the electricity loads from month D to month 12; Q L (d) represents the load electricity in month d; Q R NE，1(D) represents the initial value for optimizing the monthly trading volume of new energy in month D, and its value shall not exceed the theoretical new energy volume in month D; Q R NE Q represents the annual trading volume of new energy; R NE (d) represents the electricity volume of new energy transactions that have been completed in month d.

[0029] Furthermore, the daily results are compared with the day-ahead spot price, and the deviation handling strategy for wind and solar power plants is determined based on the comparison results, including:

[0030] Compare the daily breakdown results with the day-ahead spot price;

[0031] When the day-ahead spot price of a wind farm or photovoltaic power station is greater than the decomposed day-ahead result, charging power is provided to the shared energy storage.

[0032] When the day-ahead spot price of a wind farm or photovoltaic power station is less than the day-to-day result, the shared energy storage provides the discharge power.

[0033] In a second aspect, the present invention provides a deviation processing device for wind and solar power plants based on shared energy storage, comprising:

[0034] The composition of the wind-solar-storage cluster is determined, including several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system.

[0035] Based on wind-solar-storage clusters and deviation mutual guarantees, and with the goal of maximizing the revenue of shared energy storage operators, a deviation mutual guarantee pricing model for shared energy storage operators is constructed, and the constraints of the shared energy storage operator deviation mutual guarantee pricing model are determined. Based on the constraints, the shared energy storage operator deviation mutual guarantee pricing model is solved to obtain the shared energy storage operator pricing, and the day-ahead spot price is determined based on the shared energy storage operator pricing.

[0036] Obtain relevant historical data of wind and solar power stations, decompose the medium- and long-term power generation of wind and solar power stations to the daily level based on the historical data, and obtain the daily decomposition results; compare the daily decomposition results with the day-ahead spot market, and determine the deviation handling strategy for wind and solar power stations based on the comparison results.

[0037] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the wind and solar power plant deviation processing method described above.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the wind and solar power plant deviation processing method described above.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The proposed method for handling deviations in wind and solar power plants introduces shared energy storage. It fully utilizes the output characteristics and complementary processing capabilities of new energy power plants to achieve deviation compensation with shared energy storage, thereby optimizing the control of both. This method coordinates the output and energy storage resources of various new energy power plants, maximizing the synergistic complementarity between wind and solar power plants and shared energy storage, and achieving the goal of maximizing the benefits for both the wind and solar power plant cluster and the shared energy storage. Simultaneously, it meets the requirements for volatility and relative tracking error, extending the lifespan of energy storage. This solution improves energy efficiency and reduces operating costs through precise control and optimization of wind, solar, and energy storage resource utilization. The wind and solar power plant deviation handling device, electronic equipment, and computer-readable storage medium provided by this invention also solve the problems raised in the background section. Attached Figure Description

[0041] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0042] Figure 1 A flowchart illustrating the deviation handling method for wind and solar power plants based on shared energy storage provided by this invention;

[0043] Figure 2 This is a schematic diagram illustrating the energy exchange relationship between wind and solar power fields and shared energy storage in this invention.

[0044] Figure 3 This is a structural block diagram of a wind and solar power station deviation processing device based on shared energy storage according to an embodiment of the present invention;

[0045] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0046] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0047] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0048] Example 1

[0049] This invention provides a method for handling deviations in wind and solar power plants based on shared energy storage, comprising the following steps:

[0050] S1. Determine the composition of the wind-solar-storage cluster, which includes several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system.

[0051] S2. Based on wind-solar-storage clusters and deviation mutual guarantee, with the goal of maximizing the revenue of shared energy storage operators, construct a shared energy storage operator deviation mutual guarantee pricing model and determine the constraints of the shared energy storage operator deviation mutual guarantee pricing model; solve the shared energy storage operator deviation mutual guarantee pricing model based on the constraints to obtain the shared energy storage operator pricing, and determine the day-ahead spot price based on the shared energy storage operator pricing.

[0052] The shared energy storage operator deviation mutual guarantee pricing model is as follows:

[0053]

[0054] Where R represents the revenue of shared energy storage operators, A represents the deviation insurance pricing standard for installed capacity of multiple market participants, G represents the installed capacity of multiple market participants purchasing deviation insurance services, and C represents the installed capacity of multiple market participants purchasing deviation insurance services. LCOS denoted as the annual levelized unit cost of energy storage; x represents the installed capacity of the energy storage power station; F(x) represents the annual deviation assessment cost when the installed capacity of energy storage is x.

[0055] The installed capacity G of multiple market participants purchasing deviation insurance services, and the annual standardized unit cost of energy storage C. LCOS The annual deviation assessment cost F(x) for an energy storage installed capacity of x is expressed as follows:

[0056]

[0057] Where q represents the number of market participants purchasing deviation insurance services; l represents the starting day; G 0 j C represents the installed capacity of the j-th market entity represented by the shared energy storage operator; V C represents the investment cost per unit of energy storage capacity. M The operating cost per unit of energy storage capacity; C H Maintenance cost per unit energy storage capacity; C E denoted as the decommissioning cost per unit of energy storage capacity; r represents the expected financial internal rate of return for shared energy storage operators; n represents the operating life of the energy storage power station; f(x) represents the typical daily deviation assessment cost when the installed energy storage capacity is x; m represents the total number of days in the assessment period; D θ It indicates the number of days in each of the four seasons of spring, summer, autumn, and winter within a year.

[0058] The constraints of the shared energy storage operator deviation mutual guarantee pricing model include: insurance cost constraints and shared energy storage power station charging and discharging constraints; the shared energy storage power station charging and discharging constraints include instantaneous charging and discharging power constraints, energy storage installed capacity constraints, and charging and discharging conversion efficiency constraints.

[0059] Insurance cost constraints include:

[0060]

[0061] Where A represents the deviation insurance pricing standard (yuan / MW·year) for installed capacity of multiple market entities; G 0 j Let F represent the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator; and the deviation assessment fee F of the j-th market entity in one year. 0 Yj C LCOS This represents the average annual cost per unit of energy storage (RMB / MW·year); x j The energy storage capacity built for the j-th market entity.

[0062] S3. Obtain relevant historical data of wind and solar power stations, decompose the medium and long-term electricity of wind and solar power stations to the day based on the historical data, and obtain the decomposition results to the day; compare the decomposition results to the day with the day-ahead spot market, and determine the deviation handling strategy for wind and solar power stations based on the comparison results.

[0063] Specifically, based on historical data from wind and solar power stations, the medium- and long-term electricity generation is broken down to the daily level, resulting in the following daily breakdown:

[0064] The proportion of theoretical renewable energy power and load power for each month is determined based on the medium- and long-term power generation of wind and solar power stations.

[0065] Using the average of the theoretical electricity share and load electricity share of new energy in each month as the allocation ratio, the annual trading volume of new energy is roughly divided to obtain the initial value of the optimized trading volume of new energy in each month, and is updated monthly based on the implementation status. If part of the annual trading volume has been completed from month 1 to month D-1, then the initial value of the monthly trading volume of new energy in month D is allocated as follows:

[0066]

[0067] Based on the monthly transaction volume breakdown, daily volume proportions are set, with weekday proportion α and Saturday / Sunday proportion β. The daily contract volume breakdown is as follows:

[0068] Breakdown of weekday electricity consumption:

[0069] Weekend daily electricity consumption breakdown:

[0070] In the formula: a NE (D) represents the proportion of the theoretical renewable energy power generation in month D to the sum of the theoretical renewable energy power generation from month D to month 12; Q NE* (d) represents the theoretical renewable energy power generation for month d, which can be obtained by accumulating the theoretical renewable energy power value over time periods; a L (D) represents the proportion of the electricity load in month D to the sum of the electricity loads from month D to month 12; Q L (d) represents the load electricity in month d; Q R NE，1 (D) represents the initial value for optimizing the monthly trading volume of new energy in month D, and its value shall not exceed the theoretical new energy volume in month D; Q R NE Q represents the annual trading volume of new energy; R NE (d) represents the electricity volume of new energy transactions that have been completed in month d.

[0071] Specifically, the daily results will be compared with the day-ahead spot prices, and the deviation handling strategy for wind and solar power plants will be determined based on the comparison results, including:

[0072] Compare the daily breakdown results with the day-ahead spot price;

[0073] When the day-ahead spot price of a wind farm or photovoltaic power station is greater than the decomposed day-ahead result, charging power is provided to the shared energy storage.

[0074] When the day-ahead spot price of a wind farm or photovoltaic power station is less than the day-to-day result, the shared energy storage provides the discharge power.

[0075] See Figure 1 In one optional embodiment, a deviation handling method for wind and solar power stations based on shared energy storage is also provided, which further explains and illustrates the above scheme, including the following steps:

[0076] Step 101: Establish a wind-solar-storage cluster including several wind and solar power stations and shared energy storage. The wind and solar power stations achieve a deviation mutual protection mode through the charging and discharging strategies of the shared energy storage.

[0077] Step 102: Based on the established wind and solar power plant cluster and the deviation mutual guarantee interaction system of shared energy storage, construct a deviation mutual guarantee pricing model for shared energy storage operators with the goal of maximizing the revenue of shared energy storage operators.

[0078] Step 103: Collect relevant historical data of wind and solar power stations, and break down the medium- and long-term electricity generation of wind and solar power stations into daily amounts based on the historical data.

[0079] Step 104: Compare the medium- and long-term electricity decomposition results with the day-ahead spot market, and comprehensively consider the pricing of shared energy storage operators to derive a deviation handling strategy for wind and solar power plants.

[0080] The preferred scheme also includes step 105: establishing a revenue model between the shared energy storage operator and the wind-solar cluster, verifying the revenue of both the shared energy storage operator and the wind-solar power station, and ensuring that the revenue of the shared energy storage operator is maximized and that the model maximizes the revenue of the wind-solar power station. Step 105 establishes the revenue model between the shared energy storage operator and the wind-solar cluster to verify and determine that the deviation handling method obtained in step 104 will maximize the revenue of both parties.

[0081] In one optional scheme, step 101 specifically includes: establishing a wind-solar-storage cluster comprising several wind and solar power stations and shared energy storage, wherein the wind and solar power stations achieve a deviation mutual protection mode through the charging and discharging strategies of the shared energy storage. For example... Figure 2 As shown, the wind-solar-storage cluster includes wind power plant clusters and solar power plant clusters. The wind, solar and storage clusters share energy storage and power transfer with the main power grid.

[0082] In one alternative, step 102 specifically includes: constructing a shared energy storage operator deviation mutual guarantee pricing model with the goal of maximizing the revenue of the shared energy storage operator. According to the shared energy storage operator deviation mutual guarantee pricing model, the revenue of the shared energy storage operator is the premium collected, and the cost includes the construction cost of the shared energy storage power station and the possible deviation assessment fees.

[0083] The shared energy storage operator deviation mutual guarantee pricing model is as follows:

[0084]

[0085] Where R represents the revenue of the shared energy storage operator, A represents the deviation insurance pricing standard (yuan / MW·year) for the installed capacity of multiple market entities, G represents the installed capacity (MW) of multiple market entities purchasing deviation insurance services, and C represents the installed capacity (MW) of the multiple market entities purchasing deviation insurance services. LCOS denoted as the annual levelized unit cost of energy storage (yuan / MW·year); x represents the installed capacity of the energy storage power station (MW); F(x) represents the annual deviation assessment cost when the installed capacity of energy storage is x.

[0086]

[0087] Where G represents the installed capacity (MW) of the multiple market participants purchasing deviation insurance services; q represents the number of multiple market participants purchasing deviation insurance services; l represents the start date; G 0 j C represents the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator; LCOS This represents the average annual cost per unit of energy storage (RMB / MW·year); C VC represents the investment cost per unit of energy storage capacity. M The operating cost per unit of energy storage capacity; C H Maintenance cost per unit energy storage capacity; C E denoted as , where is the cost of scrapping a unit of energy storage capacity; r represents the expected internal rate of return for shared energy storage operators; n represents the operating life of the energy storage power station; F(x) represents the annual deviation assessment cost when the installed energy storage capacity is x; f(x) represents the typical daily deviation assessment cost when the installed energy storage capacity is x; m represents the total number of days in the assessment period; D θ It indicates the number of days in each of the four seasons of spring, summer, autumn, and winter within a year;

[0088] The deviation electricity matrix of the j-th multi-market entity is:

[0089]

[0090] in, This represents the deviation electricity matrix for the j-th multi-market entity; N represents the deviation in electricity volume of the j-th multi-market participant during time period i; N represents the end of the time period.

[0091] α j G represents the average load factor for the j-th time period within a typical day; 0 j X represents the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator; T represents the transpose matrix; X represents the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator. j 0 This represents the average deviation rate for that period within a typical day;

[0092] The typical daily shared energy storage operator's deviation power (i.e., energy storage charging and discharging demand) matrix for each time period is as follows:

[0093]

[0094] in, This represents the deviation power matrix for a typical daily shared energy storage operator at different time periods; This represents the sum of the deviations in electricity volume among various multi-market participants during time period i of a typical day for a shared energy storage operator;

[0095] Let N represent the deviation in electricity volume of the j-th multi-market entity during time period i; N represents the end of time period; and I represents the start of time period.

[0096] The shared energy storage power station responds instantly based on the real-time deviation of the sum of wind and solar power station clusters, taking into account constraints such as instantaneous charging and discharging power, installed energy storage capacity, and charging / discharging conversion efficiency. When the installed capacity of the shared energy storage power station is x, the charging time is t, the charging capacity is tx, and the amount of electricity stored by the energy storage power station in the i-th time period is L. i Qd Let be the state matrix of the energy storage power station over m time periods; and let be the maximum charging power x(t) during time period i. i -ti -1 Maximum discharge power Discharge capacity Rechargeable capacity (tx-L) i The charging and discharging matrix of the energy storage power station is derived as follows:

[0097] Q D =(L1,L2,...,L i ,...,L m )

[0098] Among them, Q d Let L be the state matrix of the energy storage power station over m time periods; i This represents the amount of electricity stored in the energy storage station at time i.

[0099]

[0100] In the formula, f(x) represents the typical daily deviation assessment cost when the installed capacity of energy storage is x; b, m, and l represent the assessment cost specified by the power grid dispatching agency, namely the deviation assessment unit price b yuan / kWh, the end time period, and the start time period, respectively.

[0101] Add constraints, including:

[0102] To ensure that new energy companies are willing to purchase deviation insurance, the insurance premium paid by them should be less than the potential deviation assessment costs and less than the cost of self-built energy storage. The deviation assessment cost F for the j-th market entity in one year... 0 Yj The energy storage capacity x built by the jth market entity j .

[0103]

[0104] Where A represents the deviation insurance pricing standard (yuan / MW·year) for installed capacity of multiple market entities; G 0 j Let F represent the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator; and the deviation assessment fee F of the j-th market entity in one year. 0 Yj C LCOS This represents the average annual cost per unit of energy storage (RMB / MW·year); x j The energy storage capacity built for the j-th market entity.

[0105] The constraints on the charging and discharging of shared energy storage power stations mainly include constraints on instantaneous charging and discharging power, energy storage installed capacity, and charging and discharging conversion efficiency. The installed capacity of the energy storage power station is x, and the charging command X for the energy storage power station during time period i is... in i Discharge command X out i The charge-discharge conversion efficiency of energy storage power stations

[0106]

[0107] Energy storage operation constraints:

[0108] Pmax≥0

[0109] 0≤x≤max{|W fd总 (t1)|,|W fd总 (t2)|,...,|W fd总 (t n )|}

[0110] In the formula, x represents the energy storage capacity of a single power station, in kWh; W fd (t) represents the prediction bias; W fd总 Let t be the cumulative deviation of market participants' predicted electricity volume.

[0111] In one optional embodiment, step 103 specifically includes:

[0112] The specific method for decomposing medium- and long-term electricity generated by wind and solar power plants is as follows:

[0113] Considering the monthly electricity matching between renewable energy and load, the annual transaction volume of renewable energy is broken down into monthly amounts.

[0114] Using the average of the theoretical electricity share and load electricity share of new energy in each month as the allocation ratio, the annual trading volume of new energy is roughly divided to obtain the initial value of the optimized trading volume of new energy in each month, and is updated monthly based on the implementation status. If part of the annual trading volume has been completed from month 1 to month D-1, then the initial value allocation of the monthly trading volume of new energy in month D is as follows:

[0115]

[0116] In the formula: a NE (D) represents the proportion of the theoretical renewable energy power generation in month D to the sum of the theoretical renewable energy power generation from month D to month 12; Q NE* (d) represents the theoretical renewable energy power generation for month d, which can be obtained by accumulating the theoretical renewable energy power value over time periods; a L (D) represents the proportion of the electricity load in month D to the sum of the electricity loads from month D to month 12; QL (d) represents the load electricity in month d; Q R NE，1 (D) represents the initial value for optimizing the monthly trading volume of new energy in month D, and its value shall not exceed the theoretical new energy volume in month D; Q R NE Q represents the annual trading volume of new energy; R NE (d) represents the electricity volume of new energy transactions that have been completed in month d.

[0117] Based on the monthly transaction volume breakdown, daily volume proportions are set, with weekday proportion α and Saturday / Sunday proportion β. The daily contract volume breakdown is as follows:

[0118] Breakdown of weekday electricity consumption:

[0119] Weekend daily electricity consumption breakdown:

[0120] In one optional embodiment, step 104 specifically includes:

[0121] The results of breaking down medium- and long-term electricity demand into daily values ​​are compared with the day-ahead spot market.

[0122] When the day-ahead spot demand of a wind farm or photovoltaic power station exceeds the medium- and long-term electricity output allocated to the day, charging power is provided to the shared energy storage; when the day-ahead spot demand of a wind farm or photovoltaic power station is less than the medium- and long-term electricity output allocated to the day, the shared energy storage provides discharging power.

[0123] In a preferred embodiment, step 105 is further included: Based on the technical problems already solved by the above solutions, a revenue model for shared energy storage operators and wind-solar clusters is established to verify the effectiveness of the deviation handling strategy, specifically as follows:

[0124] Establish a revenue model for shared energy storage operators, specifically as follows:

[0125] B ess =B ess-ren +B ess-rv -C ess-om -C ess-gr -C ess-ab -C ope-sere

[0126] Among them, B ess Indicates the revenue of the shared operator; B ess-ren This refers to the rental revenue collected by the operator from the user; B ess-rv C represents the residual value income of shared energy storage; ess-om Indicates the operation and maintenance cost of shared energy storage stations; C ess-gr The cost C of the shared energy storage station to purchase electricity from the grid is indicated.ess-ab This indicates the cost for operators to purchase wind or solar power from users; C ope-sere This indicates the cost of operator service fees;

[0127] Establish a revenue model for wind and solar power plants, specifically as follows:

[0128] The actual profits of wind farms and photovoltaic power plants consist of market revenue, ESSA energy trading revenue and costs, output deviation penalties, transmission costs, and SES operation and maintenance costs, which can be expressed as:

[0129]

[0130] in This represents the total profit of new energy power plant i during time period t; This indicates the revenue obtained by a new energy power plant from participating in day-ahead energy market bidding during time period t; This represents the revenue that a new energy power station gains from providing charging power to shared energy storage during time period t. This represents the cost required for a new energy power plant to share energy storage to provide discharge power during time period t; This represents the penalty cost for handling deviations at a new energy power plant during time period t. This represents the power transmission cost of a new energy power plant during time period t; This represents the SES operation and maintenance cost of a new energy power plant during time period t.

[0131] By verifying the revenue of shared energy storage operators and wind and solar power plants, we can ensure that the revenue of shared energy storage operators is maximized and that the model maximizes the revenue of wind and solar power plants.

[0132] Example 2

[0133] like Figure 3 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a wind and solar power station deviation processing device based on shared energy storage, comprising:

[0134] The composition of the wind-solar-storage cluster is determined, including several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system.

[0135] Based on wind-solar-storage clusters and deviation mutual guarantees, and with the goal of maximizing the revenue of shared energy storage operators, a deviation mutual guarantee pricing model for shared energy storage operators is constructed, and the constraints of the shared energy storage operator deviation mutual guarantee pricing model are determined. Based on the constraints, the shared energy storage operator deviation mutual guarantee pricing model is solved to obtain the shared energy storage operator pricing, and the day-ahead spot price is determined based on the shared energy storage operator pricing.

[0136] Obtain relevant historical data of wind and solar power stations, decompose the medium- and long-term power generation of wind and solar power stations to the daily level based on the historical data, and obtain the daily decomposition results; compare the daily decomposition results with the day-ahead spot market, and determine the deviation handling strategy for wind and solar power stations based on the comparison results.

[0137] Example 3

[0138] like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a deviation processing method for wind and solar power plants based on shared energy storage;

[0139] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0140] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the deviation processing method for wind and solar power stations based on shared energy storage in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0141] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0142] At least one processor 102 may 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. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0143] The memory 101 in the electronic device 100 stores multiple instructions to implement a deviation processing method for a wind and solar power station based on shared energy storage, and the processor 102 can execute multiple instructions to achieve the following:

[0144] The composition of the wind-solar-storage cluster is determined, including several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system.

[0145] Based on wind-solar-storage clusters and deviation mutual guarantees, and with the goal of maximizing the revenue of shared energy storage operators, a deviation mutual guarantee pricing model for shared energy storage operators is constructed, and the constraints of the shared energy storage operator deviation mutual guarantee pricing model are determined. Based on the constraints, the shared energy storage operator deviation mutual guarantee pricing model is solved to obtain the shared energy storage operator pricing, and the day-ahead spot price is determined based on the shared energy storage operator pricing.

[0146] Obtain relevant historical data of wind and solar power stations, decompose the medium- and long-term power generation of wind and solar power stations to the daily level based on the historical data, and obtain the daily decomposition results; compare the daily decomposition results with the day-ahead spot market, and determine the deviation handling strategy for wind and solar power stations based on the comparison results.

[0147] Example 4

[0148] If the modules / units integrated in the electronic device 100 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 executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0149] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," 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 the invention. In this specification, 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.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for handling deviations in wind and solar power plants based on shared energy storage, characterized in that, include: The composition of the wind-solar-storage cluster is determined, including several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system. Based on wind-solar-storage clusters and deviation mutual guarantee, and with the goal of maximizing the revenue of shared energy storage operators, a deviation mutual guarantee pricing model for shared energy storage operators is constructed. The specific model is as follows: R represents the revenue of the shared energy storage operator; A represents the deviation insurance pricing standard for multiple market participants' unit installed capacity; G represents the installed capacity of multiple market participants purchasing deviation insurance services; CLCOS represents the annual levelized unit installed cost of energy storage; x represents the installed capacity of the energy storage power station; F(x) represents the annual deviation assessment fee when the installed capacity of the energy storage is x; where the revenue of the shared energy storage operator is the premium collected, and the cost includes the construction cost of the shared energy storage power station and the possible deviation assessment fee; the constraints of the model include insurance fee constraints, shared energy storage power station charging and discharging constraints, and shared energy storage power station charging and discharging constraints include instantaneous charging and discharging power constraints, energy storage installed capacity constraints, and charging and discharging conversion efficiency constraints; based on the above constraints, the deviation mutual insurance pricing model is solved to obtain the pricing of the shared energy storage operator, that is, the deviation insurance pricing standard A for multiple market participants' unit installed capacity; the day-ahead spot price is determined based on the pricing of the shared energy storage operator; Obtain relevant historical data of wind and solar power stations, decompose the medium- and long-term power generation of wind and solar power stations to the daily level based on the historical data, and obtain the daily decomposition results; compare the daily decomposition results with the day-ahead spot market, and determine the deviation handling strategy for wind and solar power stations based on the comparison results.

2. The method for handling deviations in wind and solar power stations according to claim 1, characterized in that, The installed capacity G of multiple market participants purchasing deviation insurance services, the annual standardized unit cost of energy storage (CLCOS), and the annual deviation assessment cost F(x) when the installed capacity of energy storage is x are expressed as follows: ； Where q represents the number of market participants purchasing deviation insurance services; l represents the starting day; G 0 j denoted as , where CV is the investment cost per unit of energy storage capacity; CM is the operating cost per unit of energy storage capacity; CH is the maintenance cost per unit of energy storage capacity; CE is the scrapping cost per unit of energy storage capacity; r represents the expected internal rate of return of the shared energy storage operator; n represents the operating life of the energy storage power station; f(x) represents the typical daily deviation assessment cost when the installed energy storage capacity is x; m represents the total number of days in the assessment period; Dθ represents the number of days in each of the four seasons (spring, summer, autumn, and winter) within a year.

3. The method for handling deviations in wind and solar power stations according to claim 2, characterized in that, The constraints of the shared energy storage operator deviation mutual guarantee pricing model are determined, where: The constraints include: insurance cost constraints and shared energy storage power station charging and discharging constraints; the shared energy storage power station charging and discharging constraints include instantaneous charging and discharging power constraints, energy storage installed capacity constraints, and charging and discharging conversion efficiency constraints.

4. The method for handling deviations in wind and solar power stations according to claim 3, characterized in that, Insurance cost constraints include: ； ； Where A represents the deviation insurance pricing standard for installed capacity of multiple market entities, in yuan / MW·year; G 0 j This represents the installed capacity (MW) of the j-th market entity represented by the shared energy storage operator; F represents the deviation assessment fee F for the j-th market entity in one year. 0 Yj CLCOS represents the annual levelized unit cost of energy storage, in yuan / MW·year; xj represents the energy storage capacity built by the j-th market entity.

5. The method for handling deviations in wind and solar power stations according to claim 4, characterized in that, Obtain relevant historical data for wind and solar power plants, and break down the medium- and long-term electricity generation of wind and solar power plants into daily amounts based on this historical data, obtaining the daily breakdown results, including: The proportion of theoretical renewable energy power and load power for each month is determined based on the medium- and long-term power generation of wind and solar power stations. Using the average of the theoretical electricity share and load electricity share of new energy in each month as the allocation ratio, the annual trading volume of new energy is roughly divided to obtain the initial value of the optimized trading volume of new energy in each month, and is updated monthly based on the implementation status. If part of the annual trading volume has been completed from month 1 to month D-1, then the initial value of the monthly trading volume of new energy in month D is allocated as follows: ； ； ； Based on the monthly transaction volume breakdown, daily volume proportions are set, with weekday proportion α and Saturday / Sunday proportion β. The daily contract volume breakdown is as follows: Breakdown of weekday electricity consumption: ; Weekend daily electricity consumption breakdown: ; In the formula: aNE (D) is the proportion of the theoretical renewable energy power in month D to the sum of the theoretical renewable energy power from month D to month 12; QNE* (D) is the theoretical renewable energy power in month D, which can be obtained by accumulating the theoretical renewable energy power value over time periods; aL (D) is the proportion of the load power in month D to the sum of the load power from month D to month 12; QL (D) is the load power in month D; QRNE,1 (D) is the initial value of the optimized monthly renewable energy trading power in month D, and its value does not exceed the theoretical renewable energy power in month D; QRNE represents the annual renewable energy trading power; QRNE (D) represents the renewable energy trading power that has been completed in month D.

6. The method for handling deviations in wind and solar power stations according to claim 5, characterized in that, The decomposed daily results are compared with the day-ahead spot price. Based on the comparison results, a deviation handling strategy for wind and solar power plants is determined, including: Compare the daily breakdown results with the day-ahead spot price; When the day-ahead spot price of a wind farm or photovoltaic power station is greater than the decomposed day-ahead result, charging power is provided to the shared energy storage. When the day-ahead spot price of a wind farm or photovoltaic power station is less than the day-to-day result, the shared energy storage provides the discharge power.

7. A deviation processing device for wind and solar power stations based on shared energy storage, characterized in that, include: The cluster determination module is used to determine the composition of the wind-solar-storage cluster, which includes several wind and solar power stations and a shared energy storage system; wherein, the wind and solar power stations mutually protect each other through the charging and discharging strategies of the shared energy storage system. The model building and solution module is used to construct a pricing model for shared energy storage operators' deviation mutual guarantee based on wind-solar-storage clusters and deviation mutual guarantee, with the goal of maximizing the revenue of shared energy storage operators. The specific model is as follows: R represents the revenue of the shared energy storage operator; A represents the deviation insurance pricing standard for multiple market participants' unit installed capacity; G represents the installed capacity of multiple market participants purchasing deviation insurance services; CLCOS represents the annual levelized unit installed cost of energy storage; x represents the installed capacity of the energy storage power station; F(x) represents the annual deviation assessment fee when the installed capacity of energy storage is x. The revenue of the shared energy storage operator is the premium collected, and the cost includes the construction cost of the shared energy storage power station and potential deviation assessment fees. The constraints of the model include insurance premium constraints and shared energy storage power station charging and discharging constraints. The charging and discharging constraints of the shared energy storage power station include instantaneous charging and discharging power constraints, energy storage installed capacity constraints, and charging and discharging conversion efficiency constraints. Based on the above constraints, the deviation mutual insurance pricing model is solved to obtain the shared energy storage operator's pricing, i.e., the deviation insurance pricing standard A for multiple market participants' unit installed capacity. The day-ahead spot price is determined based on the shared energy storage operator's pricing. The strategy determination module is used to acquire relevant historical data of wind and solar power plants, decompose the medium and long-term electricity of wind and solar power plants into daily amounts based on the historical data, and obtain the decomposition results to the day. The decomposition results to the day are compared with the day-ahead spot market, and the deviation handling strategy of wind and solar power plants is determined based on the comparison results.

8. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the wind and solar power plant deviation processing method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the wind and solar power plant deviation processing method as described in any one of claims 1 to 6.

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