Operation mode design method and device for participation of energy storage operator in electricity market, medium and product

Abstract

The invention discloses an operation mode design method and device for an energy storage operator to participate in a power market, a medium and a product, and relates to the field of independent energy storage, and the method comprises the steps: constructing a transaction and operation strategy model for the energy storage operator to participate in the power market, and a demand response model based on a price basis; determining the electricity purchasing demand of the energy storage operator according to the transaction and operation strategy model of the energy storage operator participating in the electricity market and the demand response model based on the price basis; constructing an electricity purchase optimization model of the energy storage operator based on the electricity purchase demand of the energy storage operator; performing uncertainty analysis on the basis of the power purchase optimization model of the energy storage operator to obtain an uncertainty random opportunity constraint power purchase optimization model; and generating an operation strategy that the energy storage operator participates in the electricity market based on the uncertainty random opportunity constraint electricity purchase optimization model. According to the invention, the operation risk caused by the uncertainty of clean energy power generation can be reduced, and the reasonability of an energy storage operator participating in the power market operation is improved.

Description

Technical Field

[0001] The present application relates to the field of independent energy storage, and in particular to a method, equipment, medium and product for designing an operation model for an energy storage operator to participate in the electricity market. Background Art

[0002] With the in-depth implementation of power market policies, new power operators have sprung up, and the market competition has become increasingly fierce. Faced with this situation, operators with independent energy storage systems are not only stable suppliers of electricity to end users in the market, but also demanders who can flexibly adjust electricity in the power market. The sustainable development path of these energy storage operators is deeply affected by the interweaving of multiple factors such as power supply and demand fluctuations, electricity price dynamics, innovation in operation strategies and trading models, and flexibility in power purchasing capabilities. Some existing studies have considered how power retailers with self-provided generators can participate in the power market through various demand responses such as price and incentives, and have constructed a power sales optimization mechanism with revenue maximization or cost minimization as the objective function. However, from the perspective of power purchase, it has not considered how power retailers with energy storage systems can flexibly participate in the power purchase industry without generators. Although some other studies have considered the impact of price and supply and demand uncertainty on power purchase and sales strategies, and proposed a variety of optimization strategies to deal with uncertainty, there is still no clear strategy for how power retailers can use flexible energy storage systems while ensuring clean energy consumption to mitigate uncertainty risks. Summary of the invention

[0003] The purpose of this application is to provide an operation model design method, equipment, medium and product for energy storage operators to participate in the electricity market, which can reduce the operational risks brought by the uncertainty of clean energy power generation and improve the rationality of energy storage operators' participation in electricity market operations.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In the first aspect, the present application provides a method for designing an operation model for an energy storage operator to participate in the electricity market, including:

[0006] Construct a transaction and operation strategy model for energy storage operators to participate in the electricity market and a price-based demand response model;

[0007] Determine the power purchase demand of the energy storage operator based on the transaction and operation strategy model of the energy storage operator participating in the power market and the price-based demand response model;

[0008] Building an energy storage operator's electricity purchasing optimization model based on the energy storage operator's electricity purchasing demand;

[0009] Perform uncertainty analysis on the basis of the energy storage operator's power purchase optimization model to obtain an uncertainty random chance constrained power purchase optimization model;

[0010] Based on the uncertainty random chance constrained electricity purchase optimization model, an operation strategy for energy storage operators to participate in the electricity market is generated.

[0011] Optionally, determining the power purchase demand of the energy storage operator according to the transaction and operation strategy model of the energy storage operator participating in the power market and the price-based demand response model includes:

[0012] Adopting the transaction and operation strategy model of energy storage operators participating in the electricity market, determining the electricity purchase object based on the user's electricity demand;

[0013] Adopting a price-based demand response model, the energy storage operator's day-ahead transaction power purchase demand is determined based on the user's power demand;

[0014] Based on the elasticity coefficient, the day-ahead transaction power purchase demand of energy storage operators is described as an elasticity matrix;

[0015] Determining the day-ahead electricity purchase demand of the energy storage operator based on the elasticity matrix;

[0016] The power purchasing demand of the energy storage operator is determined based on the power purchasing demand of the energy storage operator's day-ahead transaction.

[0017] Optionally, the elastic coefficient is expressed as:

[0018]

[0019] In the formula, e i,j represents the elastic modulus, Pc i represents the initial price at time i, ΔPc i represents the price change at time i, Q i represents the initial load of the user at time i, Q j represents the initial load of the user at time j, ΔQ i represents the load change of the user at time i, ΔQ j Represents the load change of the user at time j.

[0020] Optionally, building an energy storage operator's electricity purchase optimization model based on the energy storage operator's electricity purchase demand includes:

[0021] A multi-objective optimization model is constructed with the goal of minimizing the electricity purchase cost of energy storage operators and maximizing clean energy consumption;

[0022] Construct a charging and discharging model for the energy storage system and obtain the power generated by the wind turbine and photovoltaic power;

[0023] On the basis of the multi-objective optimization model, constraints are constructed based on the power purchase demand of the energy storage operator, the power generation power of wind turbines and the power generation power of photovoltaic power; the constraints include: supply and demand balance constraints, wind turbine power generation power and photovoltaic power generation power balance constraints, up and down rotation reserve constraints, thermal power units should meet the ramp, start and shutdown time constraints, load change limit constraints, power purchase constraints based on price demand response and user power consumption cost constraints and energy storage constraints;

[0024] A power purchasing optimization model of the energy storage operator is obtained based on the constraint conditions and the multi-objective optimization model.

[0025] Optionally, the multi-objective optimization model includes a first objective optimization model and a second objective optimization model.

[0026] Optionally, uncertainty analysis is performed on the basis of the energy storage operator's power purchase optimization model to obtain an uncertainty random chance constrained power purchase optimization model, including:

[0027] Perform uncertainty analysis based on the power purchase optimization model of the energy storage operator to obtain uncertainty analysis results;

[0028] The uncertainty random chance constrained electricity purchasing optimization model is constructed based on the uncertainty analysis results and the electricity purchasing optimization model of the energy storage operator.

[0029] Optionally, uncertainty analysis results obtained by performing uncertainty analysis on the basis of the power purchase optimization model of the energy storage operator include: uncertainty of wind power generation, uncertainty of photovoltaic power generation and uncertainty of post-demand response load.

[0030] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-mentioned methods for designing an operating model for energy storage operators to participate in the electricity market.

[0031] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for designing an operating model for energy storage operators to participate in the electricity market.

[0032] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for designing an operating model for an energy storage operator to participate in the electricity market.

[0033] According to the specific embodiments provided in this application, this application has the following technical effects:

[0034] This application provides a method, equipment, medium and product for designing an operation model for energy storage operators to participate in the power market. It uses the demand response mechanism to build a transaction and operation strategy model for energy storage operators to participate in the power market, and builds a demand response model based on price. At the same time, it considers uncertain random variables and builds an uncertain random opportunity constraint power purchase optimization model, which can reduce the operational risks brought by the uncertainty of clean energy generation and improve the rationality of energy storage operators participating in power market operations. In addition, it can also reduce the power purchase cost of power sales operators while ensuring clean energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flowchart of a method for designing an operation mode for an energy storage operator to participate in the electricity market provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a process for an independent energy storage operator to participate in medium- and long-term electricity trading provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of a process for an independent energy storage operator to participate in day-ahead power trading provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] In the complex context of fully considering the uncertainty of power supply and demand, building a trading strategy for independent energy storage operators to participate in the power market as power sellers, exploring and applying flexible energy storage system strategies based on price-based demand response, has become the key to reducing the operational risks brought by the volatility of clean energy power generation, ensuring efficient consumption of clean energy, and effectively controlling the power purchase costs of energy storage operators. The implementation of this strategy can not only achieve effective demand response on the power purchase side without increasing the burden of energy supply, but also lay a solid foundation for the sustainable development of energy storage operators and promote the entire power market to move towards a greener, smarter and more flexible direction.

[0042] In areas with extremely rich renewable energy resources such as wind and solar energy (for example, Qinghai and other northwestern regions of China), a wide range of application scenarios can be provided for energy storage operators. By establishing and improving independent energy storage power stations to participate in electricity trading strategies, energy storage operators in renewable energy-rich areas can participate in market transactions more flexibly and improve the economic benefits of energy storage systems.

[0043] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0044] This application takes independent energy storage operators as the research object, and the end users are industrial users. Industrial users report their electricity demand to operators, and energy suppliers are external power grids and green energy companies. Green energy companies are composed of wind power plants and photovoltaic power plants. Both types of power plants generate electricity according to natural conditions as much as possible, and set up independent energy storage operators to participate in medium- and long-term electricity transactions and day-ahead transactions.

[0045] In this application, two main goals are considered: one is to reduce the electricity purchase cost of retailers and increase their income, thereby achieving sustainable economic development; the other is to increase clean energy electricity consumption and achieve environmental sustainability. Based on this, in an exemplary embodiment, Figure 1 As shown, the present application provides a method for designing an operation mode for an energy storage operator to participate in the power market. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, including:

[0046] Step 100: Construct a transaction and operation strategy model for energy storage operators to participate in the electricity market and a price-based demand response model.

[0047] Step 101: Determine the power purchase demand of the energy storage operator based on the transaction and operation strategy model of the energy storage operator participating in the power market and the price-based demand response model.

[0048] Step 102: Building an energy storage operator's electricity purchasing optimization model based on the energy storage operator's electricity purchasing demand.

[0049] Step 103: Perform uncertainty analysis based on the energy storage operator's power purchase optimization model to obtain an uncertainty random chance constrained power purchase optimization model.

[0050] Step 104: Generate an operation strategy for the energy storage operator to participate in the electricity market based on the uncertain random chance constrained electricity purchase optimization model.

[0051] In another exemplary embodiment of the present application, the implementation process of step 101 can be described as follows:

[0052] Step 1: Use the transaction and operation strategy model of energy storage operators (hereinafter referred to as operators) in the power market to determine the power purchase object based on the user's power demand. For example:

[0053] (1) In medium and long-term power transactions (such as Figure 2 As shown in the figure, users report their total electricity demand and demand in different time periods to independent energy storage operators and sign contracts with the operators.

[0054] (2) Independent energy storage operators sign power purchase contracts with external power grids and green energy companies based on contracts with users. Power purchase contracts include total power purchase, electricity price, and daily settlement curve.

[0055] (3) Based on the electricity demand reported by users in advance, electricity is purchased from green energy companies first. Then it is determined whether the green energy company can meet the electricity demand. If not, the remaining electricity is purchased from the external power grid. According to the electricity demand, more than 80% and less than 95% of the production capacity of the green energy company are purchased to avoid the purchase cost being temporarily too high, while ensuring the consumption of clean energy power generation.

[0056] (4) When trading a day ago (e.g. Figure 3 As shown), the independent energy storage operator determines whether the medium- and long-term power purchase can meet the power demand. If not, the remaining part will be purchased from the external power grid at this stage to meet the user's power demand.

[0057] Step 2: Use a price-based demand response model to determine the energy storage operator's day-ahead electricity purchase demand based on the user's electricity demand. For example:

[0058] (1) Energy storage operators can implement a low-price charging and high-price discharging strategy while keeping the user's electricity demand unchanged, thereby reducing the high electricity purchase costs during peak hours. They can adjust their day-ahead transaction electricity purchases based on the electricity price and low electricity purchase cost targets. The operator's day-ahead transaction electricity purchase demand is calculated as follows:

[0059]

[0060] In the formula, represents the operator's electricity purchase demand at time t after energy storage is added, represents the operator's electricity demand at time t before energy storage is put into use, Represents the change in electricity demand at time t.

[0061] (2) The operator's electricity purchase demand depends on the amount of electricity contracted with the user. Users have a large electricity demand and are sensitive to electricity prices. The electricity price can be adjusted to motivate users and guide them to use electricity reasonably. Introducing time-of-use electricity prices can encourage users to reduce their electricity demand when electricity prices are high or clean energy generation is insufficient. Conversely, electricity demand will increase. In this way, operators can adjust their electricity purchase demand.

[0062] Step 3: Based on the elasticity coefficient, the energy storage operator's day-ahead transaction power purchase demand is described as an elasticity matrix. According to the elasticity matrix and economic principles, the operator's day-ahead transaction power purchase demand is described as an elasticity matrix, which is:

[0063]

[0064] Where E is the elasticity matrix, e i,j (i,j=1,2,...,n) represents the elastic coefficient, When i=j, e i,j is the self-elastic coefficient. When i≠j, e i,j is the cross elastic coefficient. Pc i represents the initial price at time i, ΔPc i represents the price change at time i, Q i represents the initial load of the user at time i, Q j represents the initial load of the user at time j, ΔQ i represents the load change of the user at time i, ΔQ j Represents the load change of the user at time j.

[0065] Step 4: Determine the energy storage operator's day-ahead electricity purchase demand, i.e., user load changes, based on the elasticity matrix.

[0066] Among them, the calculation formula for the energy storage operator's day-ahead transaction power purchase demand is expressed as:

[0067]

[0068] In the formula, Q P (t) represents the user load at time t before considering price-based demand response, Q P* (t) represents the user load at time t after considering price-based demand response.

[0069] Step 5: Determine the power purchase demand of the energy storage operator based on the power purchase demand of the energy storage operator's day-ahead transaction. This step essentially analyzes the power purchase demand based on the transaction and operation strategy. The power purchase demand is expressed as:

[0070]

[0071] Where Q(t) represents the electricity purchase demand of independent energy storage operators before considering price-based demand response. represents the electricity purchased by independent energy storage operators before considering price-based demand response, Q * (t) represents the electricity purchase demand of independent energy storage operators after considering price-based demand response, Represents the electricity purchased by independent energy storage operators after taking into account price-based demand response.

[0072] In another exemplary embodiment of the present application, the implementation process of step 102 can be described as follows:

[0073] Step 1: With the goal of minimizing the electricity purchase cost of energy storage operators and maximizing the clean energy consumption, a multi-objective optimization model is constructed. The multi-objective optimization model includes a first-objective optimization model and a second-objective optimization model.

[0074] The cost of purchasing electricity is composed of multiple costs, including the depreciation of the energy storage system, daily operation and maintenance costs, the cost of purchasing electricity from green energy companies and external power grids in medium- and long-term electricity transactions, and temporary electricity purchase costs in day-ahead transactions, as well as deviation assessment benefits. After the introduction of deviation assessment benefits, green energy companies are penalized for not meeting demand based on the results of medium- and long-term electricity transactions. For energy storage operators, this is a kind of income, which includes compensation given by green energy companies for power shortages and the price difference between medium- and long-term electricity transactions and day-ahead transactions for unmet demand. Based on this, the first objective optimization model can be expressed as:

[0075]

[0076] In the formula, f1 is the electricity purchase cost, is the energy storage depreciation cost, is the operation and maintenance cost of energy storage at time t, and They represent the electricity purchased from green energy enterprises and external power grids, as well as the electricity temporarily purchased by operators at time t before price demand response without considering the price demand response. and They represent the change in the amount of electricity purchased from green energy companies and external power grids at time t after considering the price demand response, and the amount of electricity temporarily purchased by operators. T is the total period, represents the cost of purchasing electricity from green energy companies, Indicates the operator's temporary electricity purchase cost, represents the cost of purchasing electricity from the grid, Indicates the deviation of green energy enterprise from the assessed income. min indicates the minimum.

[0077] The degree of clean energy electricity consumption can be measured by the supply-demand ratio of clean energy power generation. Based on this, the second objective optimization model can be expressed as:

[0078]

[0079] Where f2 is the clean energy supply-demand ratio, P WPP (t) is the available wind power output at time t, P PV (t) is the available photovoltaic power output at time t. max means the maximum value.

[0080] Step 2: Construct a charging and discharging model of the energy storage system and obtain the power generated by the wind turbine and the photovoltaic power. The charging and discharging model of the energy storage system is expressed as:

[0081]

[0082] In the formula, S es (t) represents the energy of the energy storage system at time t, S es (t-1) represents the energy of the energy storage system in time period t-1, δ es Represents the energy loss rate of the energy storage system. ζ represents the state variable, which is used to determine whether the energy storage system is charging at time t, 0 for discharging and 1 for charging. η esc Indicates charging power, P esc (t) represents the charging power during period t, P esd (t) represents the discharge power in period t, η esd Indicates the discharge power.

[0083] Furthermore, before obtaining the wind turbine power generation and photovoltaic power generation, the following steps may be performed:

[0084] Construct a green energy enterprise power supply model, divided into wind power plants and photovoltaic power plants.

[0085] The power generation of a wind power plant is affected by wind speed. If the wind speed is too small, the wind turbine cannot start and cannot generate electricity. If the wind speed is too large, the wind turbine will be damaged and its service life will be affected. In view of the above wind speed constraints, the power generation of the wind turbine can be expressed as:

[0086]

[0087] Where P WPP(t) represents the power generated by the wind turbine, v t represents the daytime wind speed at time t, v in Indicates the fan cut-in wind speed, P W,R Indicates the rated power of the fan, v R Indicates the rated wind speed of the fan, v out Indicates the fan cut-out wind speed.

[0088] Photovoltaic power generation is affected by solar radiation, and its power generation can be expressed as:

[0089] P PV (t) = η PV ·S PV ·θ(t).

[0090] Where η PV represents the solar radiation efficiency, S PV represents the solar radiation area, and θ(t) represents the solar radiation intensity at time t.

[0091] Step 3: Based on the multi-objective optimization model, constraints are constructed based on the power purchase demand of energy storage operators, wind turbine power generation and photovoltaic power generation. The constraints include: supply and demand balance constraints, wind turbine power generation and photovoltaic power generation balance constraints, up and down rotation reserve constraints, thermal power units should meet ramp, start and shutdown time constraints, load change limit constraints, power purchase constraints based on price demand response, user power consumption cost constraints and energy storage constraints.

[0092] Among them, (1) the supply and demand balance constraint is expressed as:

[0093]

[0094] Where P ES (t) represents the amount of electricity purchased from the external power grid at time t after considering the price demand response, The purchasing power of energy storage systems changes with the changes in green energy companies. It is the change in operator purchasing power after considering price-based demand response.

[0095] (2) In addition to the constraint that the power supply cannot exceed the maximum allowable power generation, the generator set also needs to meet the wind turbine power generation and photovoltaic power generation balance constraints, which can be expressed as:

[0096]

[0097] In the formula, is the energy reduction at time t.

[0098] (3) Spinning reserve constraints of green energy enterprises. Due to natural conditions, there is uncertainty in the supply of green electricity, which is prone to power supply deviation. In order to overcome the impact of wind power generation and photovoltaic power generation output fluctuations on the operational stability of green energy enterprises, a certain amount of power capacity needs to be reserved. The spinning reserve constraints for up and down rotation can be expressed as:

[0099]

[0100] Where P GE (t) is the electricity sales volume of green energy enterprises at time t, and is the maximum and minimum saleable electricity of the green energy enterprise at time t, is the transaction electricity between the external power grid and the green energy enterprise at time t, r1, r2, r3, r4, and r5 are all spiral reserve coefficients.

[0101] (4) Ramp, start and shutdown time constraints of thermal power. Thermal power units should meet the ramp, start and shutdown time constraints, which can be expressed as:

[0102] μ t Δg - ≤g t -g t-1 ≤μ t Δg + .

[0103]

[0104] In the formula, μ * is the operation status of the thermal power unit at time *, g * is the power generation of the thermal power unit at the moment of *, Δg - and Δg + is the maximum increase or decrease in power generation of thermal power units, and is the cumulative operating time and downtime of the thermal power unit, M on and M off It is the preparation time for starting and stopping thermal power units.

[0105] (5) Load change limit can be expressed as:

[0106] |Q(t)-Q * (t)|≤ΔQ max (t).

[0107]

[0108] Where ΔQ(t) is the load change of the user after the price-based demand response, ΔQ max (t) is the maximum load change of the user after price-based demand response.

[0109] (6) Considering the power purchase constraints and user power consumption cost based on price demand response, it can be expressed as:

[0110]

[0111] In the formula, C U (t) and It considers the user's electricity consumption cost before and after price-based demand response.

[0112] (7) Energy storage constraints. The response change must not exceed the energy storage capacity limit, which is:

[0113]

[0114] Where ΔP ES (t) is the increase in electricity during the valley period or the decrease in electricity during the peak period, It is the upper limit of energy storage capacity.

[0115] Step 4: Based on the constraints and multi-objective optimization model, the power purchase optimization model of the energy storage operator is obtained.

[0116] In another exemplary embodiment of the present application, the implementation process of step 103 can be described as follows:

[0117] Step 1: Perform uncertainty analysis based on the power purchase optimization model of the energy storage operator to obtain uncertainty analysis results. The uncertainty analysis results obtained by performing uncertainty analysis based on the power purchase optimization model of the energy storage operator include: uncertainty of wind power generation, uncertainty of photovoltaic power generation, and uncertainty of load after demand response.

[0118] (1) Uncertainty of wind power generation. The uncertainty of wind power generation depends on the uncertainty of wind speed. It is usually assumed that the wind speed follows the Weibull distribution, that is:

[0119]

[0120] Where c and k are the scale parameter and state parameter of the Weibull distribution, f(v t ) is the probability density function of the Weibull distribution of wind speed, and e is the natural logarithm.

[0121] (2) Uncertainty of photovoltaic power generation. The uncertainty of solar radiation leads to the uncertainty of photovoltaic power generation. It is usually assumed that solar radiation follows the Beta distribution, which is:

[0122]

[0123] Where f(PV(t)) is the continuous probability density distribution describing the probability of solar irradiance, α and β are the shape parameters of the Beta distribution, and δ are the mathematical expectation and standard deviation of solar radiation intensity, r is the solar radiation intensity, r max is the maximum solar radiation intensity, Γ(*) represents the Gamma function, which is an extended factorial function.

[0124] (3) Uncertainty of load after demand response. Assume that the load error follows a normal distribution with a mean of zero. have:

[0125]

[0126] Where e(t) is the load error, is a normal distribution with mean zero, is the standard deviation. k′ is the standard deviation, generally 2%-5%. L f (t) is the predicted load value.

[0127] Among them, the load value after error correction is L(t):

[0128] L(t)=L f (t)+e(t).

[0129] Step 2: Based on the uncertainty analysis results and the energy storage operator's power purchase optimization model, an uncertainty random chance constrained power purchase optimization model is constructed. The constructed uncertainty random chance constrained power purchase optimization model includes a first target optimization model and a second target optimization model after considering uncertainty.

[0130] Among them, the optimization model of the first objective after considering uncertainty is expressed as:

[0131]

[0132] In the formula, is the electricity purchase cost after considering uncertainty, α1 is the confidence level of the electricity purchase cost f1, It means that under the premise of random variables based on the model, the probability that the cost is less than the electricity purchase cost f1 is greater than or equal to α1.

[0133] The optimization model of the second objective after considering uncertainty is expressed as:

[0134]

[0135] In the formula, is the clean energy supply-demand ratio after considering uncertainty, and α2 is the confidence level of the clean energy supply-demand ratio f2.

[0136] The supply and demand equilibrium under random chance constraints can be expressed as:

[0137]

[0138] Balance of wind power and photovoltaic power generation under random chance constraints:

[0139]

[0140] The spinning reserve constraint of green energy enterprises under random chance constraints can be expressed as:

[0141]

[0142] Load variation constraints under random chance constraints:

[0143] Pr{Q(t)-Q * (t)|≤ΔQ max (t)}≥β5.

[0144]

[0145] Other constraints are consistent with the above description.

[0146] Where Pr{*} represents probability, which is the abbreviation of the word probability. β1, β2, β3, β4, β5, and β6 are all confidence levels.

[0147] Based on the above description, this application aims to reduce the operational risks brought by the uncertainty of clean energy power generation, and reduce the electricity purchase costs of power sales operators while ensuring clean energy consumption. Its main implementation process includes: building a transaction and operation strategy model for independent energy storage operators to participate in the power market. Building a demand response model based on price. Building a conventional power purchase optimization model for independent energy storage operators and constraining it. Conducting uncertainty analysis of conventional power purchase optimization models. Building an uncertainty random chance constrained power purchase optimization model and constraining it.

[0148] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the operation model design data of the energy storage operator participating in the power market. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for designing an operation model for an energy storage operator to participate in the power market is implemented.

[0149] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0150] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0151] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0152] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0153] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0154] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0155] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for designing an operation model for energy storage operators to participate in the power market, characterized in that: The operation model design method for energy storage operators to participate in the electricity market includes: Construct a transaction and operation strategy model for energy storage operators to participate in the electricity market and a price-based demand response model; Determine the power purchase demand of the energy storage operator based on the transaction and operation strategy model of the energy storage operator participating in the power market and the price-based demand response model; Building an energy storage operator's electricity purchasing optimization model based on the energy storage operator's electricity purchasing demand; Perform uncertainty analysis on the basis of the energy storage operator's power purchase optimization model to obtain an uncertainty random chance constrained power purchase optimization model; Based on the uncertainty random chance constrained electricity purchase optimization model, an operation strategy for energy storage operators to participate in the electricity market is generated.

2. The method for designing an operation model for energy storage operators to participate in the electricity market according to claim 1, characterized in that: The power purchase demand of the energy storage operator is determined based on the transaction and operation strategy model of the energy storage operator participating in the power market and the price-based demand response model, including: Adopting the transaction and operation strategy model of energy storage operators participating in the electricity market, determining the electricity purchase object based on the user's electricity demand; Adopting a price-based demand response model, the energy storage operator's day-ahead transaction power purchase demand is determined based on the user's power demand; Based on the elasticity coefficient, the day-ahead transaction power purchase demand of energy storage operators is described as an elasticity matrix; Determining the day-ahead electricity purchase demand of the energy storage operator based on the elasticity matrix; The power purchasing demand of the energy storage operator is determined based on the power purchasing demand of the energy storage operator's day-ahead transaction.

3. The method for designing an operation model for energy storage operators to participate in the electricity market according to claim 2, characterized in that: The elastic coefficient is expressed as: In the formula, e i,j represents the elastic modulus, Pc i represents the initial price at time i, ΔPc i represents the price change at time i, Q i represents the initial load of the user at time i, Q j represents the initial load of the user at time j, ΔQ i represents the load change of the user at time i, ΔQ j Represents the load change of the user at time j.

4. The method for designing an operation model for energy storage operators to participate in the electricity market according to claim 1, characterized in that: Based on the power purchasing demand of the energy storage operator, an energy storage operator's power purchasing optimization model is constructed, including: A multi-objective optimization model is constructed with the goal of minimizing the electricity purchase cost of energy storage operators and maximizing clean energy consumption; Construct a charging and discharging model for the energy storage system and obtain the power generated by the wind turbine and photovoltaic power; On the basis of the multi-objective optimization model, constraints are constructed based on the power purchase demand of the energy storage operator, the power generation power of wind turbines and the power generation power of photovoltaic power; the constraints include: supply and demand balance constraints, wind turbine power generation power and photovoltaic power generation power balance constraints, up and down rotation reserve constraints, thermal power units should meet the ramp, start and shutdown time constraints, load change limit constraints, power purchase constraints based on price demand response and user power consumption cost constraints and energy storage constraints; A power purchasing optimization model of the energy storage operator is obtained based on the constraint conditions and the multi-objective optimization model.

5. The method for designing an operation model for energy storage operators to participate in the electricity market according to claim 4, characterized in that: The multi-objective optimization model includes a first objective optimization model and a second objective optimization model.

6. The method for designing an operation model for energy storage operators to participate in the electricity market according to claim 5, characterized in that: Based on the energy storage operator's power purchase optimization model, uncertainty analysis is performed to obtain an uncertainty random chance constrained power purchase optimization model, including: Perform uncertainty analysis based on the power purchase optimization model of the energy storage operator to obtain uncertainty analysis results; The uncertainty random chance constrained electricity purchasing optimization model is constructed based on the uncertainty analysis results and the electricity purchasing optimization model of the energy storage operator.

7. The method for designing an operation model for energy storage operators to participate in the electricity market according to claim 6, characterized in that: The uncertainty analysis results obtained by performing uncertainty analysis on the basis of the power purchase optimization model of the energy storage operator include: uncertainty of wind power generation, uncertainty of photovoltaic power generation and uncertainty of post-demand response load.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for designing an operation model for energy storage operators to participate in the electricity market as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for designing an operation model for an energy storage operator to participate in the electricity market as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for designing an operation model for an energy storage operator to participate in the electricity market as described in any one of claims 1 to 7 is implemented.

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