Electric power spot market oriented electric power resource scheduling optimization method and system

Abstract

The invention provides an electric power spot market-oriented electric power resource scheduling optimization method and system. The method comprises the steps of obtaining initial capacity of each distributed resource in a virtual power plant, electricity price information of each time period of a typical day and demand response cost of the typical day; inputting the initial capacity of each distributed resource in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacity of each distributed resource in the virtual power plant; inputting the electricity price information of each time period of the typical day and the demand response cost of the typical day into a pre-established dynamic optimization transaction model to obtain an operation strategy of each distributed resource; and performing optimization scheduling on each distributed resource based on the operation strategy of each distributed resource. According to the technical scheme provided by the invention, the two-stage optimization method is adopted to perform capacity planning and operation optimization on the virtual power plant, and the stability and economy of the operation of the virtual power plant are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power resource scheduling, and in particular, to a method and system for optimizing power resource scheduling for the electricity spot market. Background Art

[0002] The electricity spot market needs to balance supply and demand in a short period of time. Participants need to continuously adjust their plans in multiple rounds of trading, resulting in high transaction costs and complexity. For small and medium-sized distributed energy and users, independently participating in the spot market faces significant technical and economic challenges. Therefore, the Virtual Power Plant (VPP) has become an important market entity. The VPP integrates dispersed distributed energy, energy storage devices, and controllable loads through information technology to form a virtual and centrally dispatched entity, which can effectively participate in power market transactions and scheduling. It not only overcomes the problem of large fluctuations in the output of individual distributed energy sources but also serves as an intermediary to provide market access and risk management support for small-scale energy.

[0003] In the electricity spot market, price fluctuations are frequent, and the uncertainty of demand and supply increases, posing great challenges to the formulation of trading strategies. The traditional power plant model and static scheduling methods are no longer able to meet the flexibility requirements of the spot market. Therefore, there is an urgent need to propose a solution that can meet the flexibility requirements of the spot market and improve the stability and economy of the operation of virtual power plants. Summary of the Invention

[0004] The present application provides a method and system for optimizing power resource scheduling for the electricity spot market to at least solve the technical problem that the low flexibility of the traditional power plant model and static scheduling methods leads to low stability and economy in the operation of virtual power plants.

[0005] A first aspect embodiment of the present application proposes a method for optimizing power resource scheduling for the electricity spot market, the method comprising:

[0006] Obtaining the initial capacities of various distributed resources in the virtual power plant, the electricity price information for each time period of a typical day, and the demand response cost of the typical day;

[0007] Inputting the initial capacities of various distributed resources in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacities of various distributed resources in the virtual power plant;

[0008] Inputting the electricity price information for each time period of the typical day and the demand response cost of the typical day into a pre-established dynamic optimization trading model to obtain the operation strategies of various distributed resources;

[0009] Optimally scheduling various distributed resources based on the operation strategies of various distributed resources;

[0010] Among them, each distributed resource in the virtual power plant includes: distributed power sources, energy storage, and gas turbines;

[0011] The distributed power sources include: wind turbine generators and photovoltaic generator sets.

[0012] Preferably, the establishment process of the virtual power plant capacity optimization model includes:

[0013] Construct a first objective function with the minimum annual economic cost of the virtual power plant as the goal;

[0014] Taking the power balance constraint of the virtual power plant, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the charging upper and lower limit constraints of the energy storage, the starting and ending state energy storage equality constraint of the energy storage, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage energy storage, the charge and discharge uniqueness constraint of the energy storage, the power purchase and sale power constraint, and the power purchase / sale uniqueness constraint as constraint conditions, and combining the first objective function to construct a virtual power plant capacity optimization model.

[0015] Furthermore, the establishment process of the dynamic optimization trading model includes:

[0016] Construct a second objective function with the minimum operating cost of the virtual power plant on a typical day as the goal;

[0017] Taking the power balance constraint of the virtual power plant, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the charging upper and lower limit constraints of the energy storage, the starting and ending state energy storage equality constraint of the energy storage, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage energy storage, the charge and discharge uniqueness constraint of the energy storage, the power purchase and sale power constraint, the power purchase / sale uniqueness constraint, and the demand response constraint as constraint conditions, and combining the second objective function to construct a virtual power plant capacity optimization model.

[0018] The second aspect of the embodiments of the present application proposes a power resource scheduling optimization system for the electricity spot market, including:

[0019] An acquisition module, configured to acquire the initial capacity of each distributed resource in the virtual power plant, the electricity price information of each time period on a typical day, and the demand response cost on a typical day;

[0020] The first optimization module is used to input the initial capacities of the distributed resources in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacities of the distributed resources in the virtual power plant;

[0021] The second optimization module is used to input the electricity price information of each time period of the typical day and the typical day demand response cost into a pre-established dynamic optimization trading model to obtain the operation strategies of the distributed resources;

[0022] The scheduling module is used to perform optimized scheduling on the distributed resources based on the operation strategies of the distributed resources;

[0023] Among them, the distributed resources in the virtual power plant include: distributed power sources, energy storage, and gas turbines;

[0024] The distributed power sources include: wind turbine generators and photovoltaic generator sets.

[0025] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in the embodiment of the first aspect is implemented.

[0026] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in the embodiment of the first aspect is implemented.

[0027] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects:

[0028] The present application proposes a power resource scheduling optimization method and system for the electricity spot market. The method includes: obtaining the initial capacities of the distributed resources in the virtual power plant, the electricity price information of each time period of the typical day, and the typical day demand response cost; inputting the initial capacities of the distributed resources in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacities of the distributed resources in the virtual power plant; inputting the electricity price information of each time period of the typical day and the typical day demand response cost into a pre-established dynamic optimization trading model to obtain the operation strategies of the distributed resources; performing optimized scheduling on the distributed resources based on the operation strategies of the distributed resources; among them, the distributed resources in the virtual power plant include: distributed power sources, energy storage, and gas turbines; the distributed power sources include: wind turbine generators and photovoltaic generator sets. The technical solution proposed by the present application uses a two-stage optimization method to perform capacity planning and operation optimization on the virtual power plant, improving the stability and economy of the virtual power plant operation.

[0029] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0030] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the drawings, wherein:

[0031] Figure 1 It is a flowchart of a method for optimizing power resource scheduling for a power spot market according to an embodiment of the present application;

[0032] Figure 2 It is a structural diagram of a system for optimizing power resource scheduling for a power spot market according to an embodiment of the present application. Detailed Embodiments

[0033] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0034] A method and system for optimizing power resource scheduling for a power spot market proposed by the present application. The method includes: obtaining the initial capacities of various distributed resources in a virtual power plant, the electricity price information for each time period of a typical day, and the typical day demand response cost; inputting the initial capacities of various distributed resources in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacities of various distributed resources in the virtual power plant; inputting the electricity price information for each time period of the typical day and the typical day demand response cost into a pre-established dynamic optimization trading model to obtain the operation strategies of various distributed resources; and performing optimized scheduling on various distributed resources based on the operation strategies of various distributed resources. Among them, various distributed resources in the virtual power plant include: distributed power sources, energy storage, and gas turbines; the distributed power sources include: wind turbine generators and photovoltaic generator sets. The technical solution proposed by the present application uses a two-stage optimization method for capacity planning and operation optimization of a virtual power plant, improving the stability and economy of the operation of the virtual power plant.

[0035] The following describes a method and system for optimizing power resource scheduling for a power spot market according to an embodiment of the present application with reference to the drawings.

[0036] Embodiment 1

[0037] Figure 1 It is a flowchart of a method for optimizing power resource scheduling for a power spot market according to an embodiment of the present application, asFigure 1 As shown, the method includes:

[0038] Step 1: Obtain the initial capacities of various distributed resources in the virtual power plant, the electricity price information for each time period of a typical day, and the demand response cost of a typical day;

[0039] Among them, the various distributed resources in the virtual power plant include: distributed power sources, energy storage, and gas turbines;

[0040] The distributed power sources include: wind turbine generators and photovoltaic generator sets.

[0041] Step 2: Input the initial capacities of various distributed resources in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacities of various distributed resources in the virtual power plant;

[0042] In the embodiments of the present disclosure, the establishment process of the virtual power plant capacity optimization model includes:

[0043] Construct a first objective function with the minimum annual economic cost of the virtual power plant as the goal;

[0044] Taking the power balance constraint of the virtual power plant, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the charging upper and lower limit constraints of the energy storage, the starting and ending state energy storage equality constraint of the energy storage, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage energy storage, the charge and discharge uniqueness constraint of the energy storage, the power constraint of power purchase and sale, and the power purchase / sale uniqueness constraint as constraint conditions, and combining the first objective function to construct a virtual power plant capacity optimization model.

[0045] Specifically, the calculation formula of the first objective function is as follows:

[0046] minF1 = C INV + C OP + C ENV - R SY

[0047] In the formula, F1 is the annual economic cost of the virtual power plant, C INV is the equipment investment and construction cost of the virtual power plant, C OP is the annual operating cost of the virtual power plant, C ENV is the annual pollutant treatment cost of the virtual power plant, R SY is the residual value income of the virtual power plant;

[0048] Among them, the calculation formula of the equipment investment and construction cost of the virtual power plant is as follows:

[0049]

[0050] In the formula, R INV,n is the annualized conversion coefficient of the investment cost of the nth type of equipment, U n is the unit capacity investment cost of the nth type of equipment in the virtual power plant, P n,rated is the capacity configured for the nth type of equipment, and N is the number of types of aggregated equipment in the virtual power plant. γ is the discount rate, L n is the service life of the nth type of equipment, L ω is the service life of the ωth type of equipment.

[0051] The calculation formula for the annual operating cost of the virtual power plant is as follows:

[0052]

[0053] In the formula, D m is the duration of the mth typical day in a year, C YW,m is the operation and maintenance cost of the generator set on the mth typical day, C ESS,m is the operation and maintenance cost of the energy storage on the mth typical day, C FUE,m is the fuel cost of the gas turbine on the mth typical day, C QT,m is the start-stop cost of the gas turbine on the mth typical day, C GRID,m is the cost of interacting with the power grid on the mth typical day, and M is the type of typical day, where M = 3, corresponding to summer, winter, and the transitional season respectively;

[0054] Among them, the calculation formula for the operation and maintenance cost of the generator set on the mth typical day is as follows:

[0055]

[0056] In the formula, λ n is the unit operation and maintenance cost of the nth type of equipment, P n,m (t) is the power of the nth type of equipment at the time point t on the mth typical day in the virtual power plant, where the equipment is a generator set.

[0057] The calculation formula for the operation and maintenance cost of the energy storage on the mth typical day is as follows:

[0058]

[0059] In the formula, is the unit operation and maintenance cost of the energy storage equipment;

[0060] The fuel cost of the gas turbine on the mth typical day

[0061]

[0062] In the formula, is the energy consumption of natural gas per standard cubic meter, is the heat generated after completely burning one standard cubic meter of natural gas, is the energy conversion efficiency of the gas turbine;

[0063] The start-stop cost of the gas turbine on the m-th typical day

[0064]

[0065] In the formula, γ QT is the start-stop cost per unit time of the gas turbine, θ QT,m (t) is the start-stop state variable of the gas turbine, taking values from 0 to 1, where 0 represents the shutdown state and 1 represents the startup state;

[0066] The calculation formula for the cost of interacting with the power grid on the m-th typical day is as follows:

[0067]

[0068] In the formula, Q GD,m (t) is the power purchase price at which the virtual power plant purchases electricity from the power grid at time t on the m-th typical day, Q SD,m (t) is the power selling price at which the virtual power plant sells electricity to the power grid at time t on the m-th typical day, P SD,m (t) is the power selling power of the power grid at time t on the m-th typical day;

[0069] The calculation formula for the annual pollutant treatment cost of the virtual power plant is as follows:

[0070]

[0071] In the formula, is the unit emission treatment cost of the j-th pollutant, Y QT,j is the emission coefficient of the j-th pollutant when the virtual power plant purchases electricity from the power grid, P GD,m (t) is the power purchase power of the power grid at time t on the m-th typical day, P QT,m (t) is the distributed gas turbine power generation power at time t on the m-th typical day, Δt is the scheduling step, Δt = 1, J is the type of pollutant, J = 3, which are carbon dioxide, sulfur dioxide, and nitrogen oxides respectively, and T is the total scheduling period, T = 24;

[0072] The calculation formula for the salvage value income of the virtual power plant is as follows:

[0073]

[0074] In the formula, ξ is the proportion of the salvage value income to the initial investment, γ is the discount rate, L nis the service life of the nth type of device.

[0075] It should be noted that R SY is only generated at the end of the last year of the life cycle; ξ is usually 5%.

[0076] The calculation formula for the power balance constraint of the virtual power plant is as follows:

[0077]

[0078] In the formula, P SD,m (t) is the power sold at time t of the mth typical day, P WP,m (t) is the power of the wind turbine at time t of the mth typical day, P PV,m (t) is the power of the photovoltaic generator set at time t of the mth typical day, P QT,m (t) is the power of the gas turbine at time t of the mth typical day, is the energy storage discharge power at time t of the mth typical day, is the energy storage charging power at time t of the mth typical day, VPPoutput is the total output power of the virtual power plant, P L,m (t) is the period load at time t of the mth typical day, is the upstream cycle load at time t of the mth typical day, is the upstream cycle load at time t of the mth typical day;

[0079] The calculation formula for the power constraint of the generator set is as follows:

[0080] θ W,P,m (t)β WP P WP,rated ≤P WP,m (t)≤θ W,P,m (t)P WP,rated

[0081] θ PV,m (t)β PV P PV,rated ≤P VP,m (t)≤θ PV,m (t)P VP,rated

[0082] In the formula, θ W,P,m (t) is the start-stop state of the wind turbine at time t of the mth typical day, β WP is the minimum load rate of the wind turbine, P WP,rated is the rated capacity of the wind turbine, θ PV,m (t) is the start-stop state of the photovoltaic generator set at time t of the mth typical day, β PV is the minimum load rate of the photovoltaic generator set, PPV,rated is the rated capacity of the photovoltaic power generation unit;

[0083] The calculation formula for the capacity constraint of the generator set is as follows:

[0084] P WP,min ≤P WP,m (t)≤ζP L (t)

[0085] P PV,min ≤P PV,m (t)≤ζP L (t)

[0086] In the formula, P WP,min is the minimum output power of the wind turbine generator set, ζ is the simultaneous proportion coefficient of the total load, P L (t) is the period load at time t, P PV,min is the minimum output power of the photovoltaic power generation unit;

[0087] The calculation formula for the power constraint of the gas turbine is as follows:

[0088] 0≤P QT,m (t)≤P QT,rated

[0089] In the formula, P QT,m (t) is the power generation power of the distributed gas turbine at time t of the mth typical day, P QT,rated is the rated capacity of the gas turbine;

[0090] The calculation formula for the up and down climbing power constraint of the gas turbine is as follows:

[0091]

[0092] In the formula, is the down climbing power limit value of the gas turbine, P QT,m (t - 1) is the power generation power of the distributed gas turbine at time t - 1 of the mth typical day, is the up climbing power limit value of the gas turbine;

[0093] The calculation formula for the equipment output constraint of the gas turbine is as follows:

[0094] θ QT,m (t)β QT P QT,rated ≤P QT,m (t)≤θ QT,m (t)P QT,rated

[0095] In the formula, θ QT,m (t) is the start-stop state of the gas turbine at time t of the mth typical day, βQT is the minimum load rate of the gas turbine;

[0096] The calculation formula for the capacity constraint of the gas turbine is as follows:

[0097]

[0098] In the formula, P QT,min is the minimum capacity of the gas turbine, is the maximum load;

[0099] The calculation formula for the energy storage balance constraint of the energy storage is as follows:

[0100]

[0101] In the formula, Q ESS,m (t) is the energy storage capacity of the energy storage at time t of the m-th typical day, Q ESS,m (t - 1) is the energy storage capacity of the energy storage at time t - 1 of the m-th typical day, η ESS,in is the charging efficiency of the energy storage, η ESS,out is the discharging efficiency of the energy storage;

[0102] The calculation formula for the upper and lower limits of charging constraint of the energy storage is as follows:

[0103] Q ESS,min P ESS,rated ≤Q ESS,m (t)≤Q ESS,max P ESS,rated

[0104] In the formula, Q ESS,min is the minimum state limit value of the energy storage capacity, P ESS,rated is the rated capacity of the energy storage, Q ESS,max is the maximum state limit value of the energy storage capacity;

[0105] The calculation formula for the equal energy storage at the start and end states of the energy storage is as follows:

[0106] Q ESS,m (0)=Q ESS,m (T - 1)

[0107] In the formula, Q ESS,m (0) is the energy storage capacity of the energy storage at the initial moment, Q ESS,m (T - 1) is the stored electricity of the energy storage in the last scheduling period of the scheduling cycle;

[0108] The calculation formula for the charging and discharging power constraint of the energy storage is as follows:

[0109]

[0110] In the formula, The charging state of the energy storage at time t for the m-th typical day, is the minimum charging power of the energy storage, P ESS,in,m (t) is the charging capacity of the energy storage at time t for the m-th typical day, is the maximum charging power of the energy storage, The discharging state of the energy storage at time t for the m-th typical day, is the minimum discharging power of the energy storage, P ESS,out,m (t) is the discharging capacity of the energy storage at time t for the m-th typical day, The discharging state of the energy storage at time t for the m-th typical day, is the maximum discharging power of the energy storage, γ ESS,in is the charging rate of the energy storage, P ESS,rated is the rated capacity of the energy storage, γ ESS,out is the discharging rate of the energy storage;

[0111] It should be noted that, respectively represent the charging and discharging states of the energy storage device at time t, represented by 0–1 variables. If the variable is 0, it means there is no charging / discharging at this time; if the variable is 1, it means there is charging / discharging at this time;

[0112] The calculation formula for the maximum discharge power constraint of the energy storage's energy storage is as follows:

[0113]

[0114] In the formula, is the maximum load;

[0115] The calculation formula for the charging and discharging uniqueness constraint of the energy storage is as follows:

[0116]

[0117] The calculation formula for the power purchase and sale power constraint is as follows:

[0118]

[0119] The calculation formula for the power purchase / sale uniqueness constraint is as follows:

[0120] 0≤θ GD,m (t)+θ SD,m (t)≤1

[0121] In the formula, θ GD,m (t) is the state variable of the virtual power plant purchasing electricity from the main network at time t for the m-th typical day, is the minimum value of the power limit for the virtual power plant to purchase electricity from the main network within time t, P GD,m(t) is the power of the virtual power plant purchasing electricity from the main grid at time t of the m-th typical day, is the maximum power limit for the virtual power plant to purchase electricity from the main grid within time t, θ SD,m (t) is the state variable of the virtual power plant selling electricity to the main grid at time t of the m-th typical day, is the minimum power limit for the virtual power plant to sell electricity to the main grid within time t, P SD,m (t) is the power of the virtual power plant selling electricity to the main grid at time t of the m-th typical day, is the maximum power limit for the virtual power plant to sell electricity to the main grid within time t.

[0122] It should be noted that θ GD,m (t) and θ SD,m (t) respectively represent the state variables of the virtual power plant purchasing and selling electricity from the main grid, which are represented by 0–1 variables. When the value is 1, it means that power purchase / sale occurs at this time; when the value is 0, it means that power purchase / sale does not occur at this time.

[0123] Step 3: Input the electricity price information and the typical day demand response cost of each time period of the typical day into a pre-established dynamic optimization trading model to obtain the operation strategies of each distributed resource;

[0124] In the embodiment of the present disclosure, the establishment process of the dynamic optimization trading model includes:

[0125] Construct a second objective function with the minimum operating cost of the virtual power plant on a typical day as the goal;

[0126] Taking the virtual power plant power balance constraint, generator set power constraint, generator set capacity constraint, gas turbine power constraint, gas turbine up and down climbing power constraint, gas turbine equipment output constraint, gas turbine capacity constraint, energy storage energy storage balance constraint, energy storage charging upper and lower limit constraint, energy storage starting and ending state energy storage equality constraint, energy storage charge and discharge power constraint, energy storage maximum discharge power constraint, energy storage charge and discharge uniqueness constraint, power purchase and sale power constraint, power purchase / sale uniqueness constraint, demand response constraint as constraint conditions, and combining the second objective function to construct a virtual power plant capacity optimization model.

[0127] It should be noted that the calculation formula of the constraint conditions of the dynamic optimization trading model is the same as that of the same constraint conditions in the virtual power plant capacity optimization model, where the capacity involved in the constraint conditions of the dynamic optimization trading model is the optimized capacity.

[0128] Specifically, the calculation formula of the second objective function is as follows:

[0129] minF OP,m =(CYW,m +C ESS,m +C FUE,m +C QT,m +C GRID,m +C DR,m )

[0130] where F OP,m is the operating cost of the virtual power plant on the m-th typical day, and C DR,m is the daily demand response cost on the m-th typical day;

[0131] Among them, the daily demand response cost on the m-th typical day includes: the upstream load transfer compensation cost and the downstream load transfer compensation cost.

[0132] The demand response constraints include: the transfer constraint of the transferable load, the transferable load balance constraint, and the upstream / downstream uniqueness constraint;

[0133] The calculation formula of the transfer constraint of the transferable load is as follows:

[0134]

[0135]

[0136] The calculation formula of the transferable load balance constraint is as follows:

[0137]

[0138] The calculation formula of the upstream / downstream uniqueness constraint is as follows:

[0139]

[0140] where is the state variable of the upstream load transfer at time t on the m-th typical day, and P L (t) is the load within time t, is the state variable of the downstream load transfer at time t on the m-th typical day, is the response ratio of the transferable load.

[0141] It should be noted that respectively represent the state variables of the upstream and downstream load transfers, which are represented by 0–1 variables.

[0142] It should be noted that during the optimization process, both capacity optimization and operation optimization involve mixed-integer non-linear programming problems. To improve the computational efficiency, a non-linear decoupling method based on constraint nesting is proposed in the literature, which transforms non-linear constraints into linear constraints, uses YALMIP for modeling, and is solved by the CPLEX solver. Through this method, the aggregation and optimization trading of virtual power plants can better adapt to the fluctuations of the electricity spot market and the demand response mechanism, and improve the overall economic efficiency.

[0143] Step 4: Optimally schedule each distributed resource based on the operation strategies of each distributed resource;

[0144] Regarding the example of capacity optimization:

[0145] A certain virtual power plant plans to participate in the electricity spot market, considering connecting 50 MW of photovoltaic power, 20 MW of wind power, 30 MW of energy storage, and several controllable load resources. Due to large fluctuations in market supply and demand and frequent occurrence of negative electricity price periods, the virtual power plant needs to balance supply and demand through precise capacity planning and maximize its economic benefits.

[0146] First, based on the historical data of the Shandong power market, including factors such as electricity price trends, power generation-side renewable energy output forecasts, and electricity loads, the model determines the optimal access resource capacity through big data analysis.

[0147] Secondly, considering the price fluctuation characteristics of the electricity spot market, the model determines the optimal capacity allocation of various resources (such as photovoltaic power, wind power, and energy storage) through optimization algorithms. For the energy storage system, the focus is on optimizing its charge and discharge strategies to increase the charging volume during negative electricity price periods and release energy during peak electricity price periods to obtain the maximum benefit.

[0148] Through model optimization, it is determined that the actual access capacity of the photovoltaic system is 40 MW, the wind power access is 15 MW, the energy storage capacity configuration is 25 MW, and the rest is used for the dynamic regulation of controllable loads. The optimized capacity configuration ensures the maximization of the economic benefits of the virtual power plant during long-term operation. Especially during negative electricity price periods, the reasonable capacity configuration of the energy storage system provides a basis for subsequent dynamic optimization operation.

[0149] Regarding the example of operation optimization:

[0150] In the daily operation of the virtual power plant, the dynamic optimization model flexibly adjusts the output and load response of various resources by real-time monitoring the market electricity price and power demand. The optimization at this stage aims to timely adjust the operation strategies of power generation and energy storage resources through market electricity price signals to achieve the dynamic trading optimization of the virtual power plant in the spot market.

[0151] In the electricity spot market on June 24, 2024, the electricity price fluctuated violently. The electricity price was negative from 8 am to 10 am, and it was the peak period from 2 pm to 5 pm, during which the electricity price rose sharply. The virtual power plant needs to adjust its power generation and energy storage strategies according to the market electricity price signal to obtain the maximum benefit.

[0152] Through the real-time electricity price and power generation load prediction of the market, the model determines to maximize the charging amount of the energy storage system during the negative electricity price period, while reducing the output of photovoltaic and wind power to avoid the power generation loss caused by negative electricity prices. During the peak electricity price period, the model schedules the energy storage system in real time to release the previously stored electric energy into the market. At the same time, for the controllable load of large users, the model triggers the demand response strategy, and through dynamic load regulation, reduces the electricity demand of users during the peak period, saves costs and obtains market benefits.

[0153] During the negative electricity price period, the charging amount of the energy storage system reached the design upper limit, avoiding the power generation loss of photovoltaic and wind power. During the peak period, the energy storage system released electricity to obtain additional benefits, and the demand-side response also reduced the load cost of the virtual power plant. Overall, through the dynamic optimization model, the virtual power plant achieved an increase in revenue in the day's spot trading.

[0154] The solution proposed in this embodiment, in the capacity planning and operation optimization of the virtual power plant, through the reasonable aggregation and dynamic optimization of the electricity spot market, effectively improves the economic benefits and operation efficiency of the virtual power plant. In the electricity spot market, with the increase in the duration of negative electricity prices and the intensification of the market supply-demand mismatch phenomenon, the virtual power plant has become an important means to cope with electricity price fluctuations and optimize the allocation of power resources. The specific advantages are as follows:

[0155] 1. The virtual power plant with energy storage can formulate reasonable charge and discharge strategies according to the real-time electricity price and actual load conditions, thereby effectively increasing the flexibility of energy flow, significantly improving the rationality of system investment and economic operation efficiency. The results show that the daily operating cost of the virtual power plant aggregating load and energy storage is 2.1% lower than that of the non-aggregated virtual power plant.

[0156] 2. The optimized operation stage considering demand response can effectively reduce the daily operating cost, optimize the user's electricity load curve, encourage users to participate in peak shaving and valley filling, and further achieve the best allocation of resources.

[0157] 3. The degree of participation in demand response has a certain impact on the operation effect of the virtual power plant. The degree of participation in demand response is negatively correlated with the daily operating cost of the system. Selecting an appropriate scale of responsive users to participate in system regulation is crucial for improving the operation economy and stability of the virtual power plant. The higher the degree of participation in demand response, the lower the daily operating cost. However, the impact on the user's energy use satisfaction cannot be ignored.

[0158] 4. The two-stage capacity planning and operation optimization model proposed in this paper first determines the optimal capacity allocation plan of resources and uses the output boundary to guide the operation of the virtual power plant, so that the optimization result is more consistent with the actual situation.

[0159] In summary, a power resource scheduling optimization method for the electricity spot market proposed in this embodiment uses a two-stage optimization method to perform capacity planning and operation optimization on the virtual power plant, improving the stability and economy of the virtual power plant operation.

[0160] Embodiment 2

[0161] Figure 2 As shown in the structure diagram of a power resource scheduling optimization system for the electricity spot market provided by an embodiment of the present application, Figure 2 as shown, the system includes:

[0162] An acquisition module 100, configured to acquire the initial capacity of each distributed resource in the virtual power plant, the electricity price information of each time period of a typical day, and the typical day demand response cost;

[0163] A first optimization module 200, configured to input the initial capacity of each distributed resource in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacity of each distributed resource in the virtual power plant;

[0164] A second optimization module 300, configured to input the electricity price information of each time period of the typical day and the typical day demand response cost into a pre-established dynamic optimization trading model to obtain the operation strategies of each distributed resource;

[0165] A scheduling module 400, configured to perform optimized scheduling on each distributed resource based on the operation strategies of each distributed resource;

[0166] Among them, each distributed resource in the virtual power plant includes: distributed power sources, energy storage, and gas turbines;

[0167] The distributed power sources include: wind turbine generators and photovoltaic generator sets.

[0168] It should be noted that the establishment process of the virtual power plant capacity optimization model includes:

[0169] Construct a first objective function with the minimum annual economic cost of the virtual power plant as the goal;

[0170] Taking the power balance constraint of the virtual power plant, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the charging upper and lower limit constraints of the energy storage, the starting and ending state energy storage equality constraint of the energy storage, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage energy storage, the charge and discharge uniqueness constraint of the energy storage, the power purchase and sale power constraint, and the power purchase / sale uniqueness constraint as the constraint conditions, and combining with the first objective function to construct a virtual power plant capacity optimization model.

[0171] The calculation formula of the first objective function is as follows:

[0172] minF1=C INV +C OP +C ENV -R SY

[0173] In the formula, F1 is the annual economic cost of the virtual power plant, C INV is the equipment investment and construction cost of the virtual power plant, C OP is the annual operation cost of the virtual power plant, C ENV is the annual pollutant treatment cost of the virtual power plant, R SY is the residual value income of the virtual power plant;

[0174] Among them, the calculation formula of the equipment investment and construction cost of the virtual power plant is as follows:

[0175]

[0176] In the formula, R INV,n is the annualized conversion coefficient of the investment cost of the nth type of equipment, U n is the unit capacity investment cost of the nth type of equipment in the virtual power plant, P n,rated is the capacity configured by the nth type of equipment, and N is the number of types of aggregated equipment in the virtual power plant;

[0177] The calculation formula of the annual operation cost of the virtual power plant is as follows:

[0178]

[0179] In the formula, D m is the duration of the mth typical day in a year, C YW,m is the operation and maintenance cost of the generator set on the mth typical day, C ESS,m is the operation and maintenance cost of the energy storage on the mth typical day, C FUE,m is the fuel cost of the gas turbine on the mth typical day, C QT,m is the start-stop cost of the gas turbine on the mth typical dayGRID,m is the cost of interacting with the power grid for the m-th typical day, where M is the type of typical day, and M = 3, corresponding to summer, winter, and transitional seasons respectively;

[0180] Among them, the calculation formula for the cost of interacting with the power grid for the m-th typical day is as follows:

[0181]

[0182] In the formula, Q GD,m (t) is the power purchase price of the virtual power plant purchasing electricity from the power grid at time t on the m-th typical day, and Q SD,m (t) is the power selling price of the virtual power plant selling electricity to the power grid at time t on the m-th typical day, and P SD,m (t) is the power selling power of the power grid at time t on the m-th typical day;

[0183] The calculation formula for the annual pollutant treatment cost of the virtual power plant is as follows:

[0184]

[0185] In the formula, is the unit emission treatment cost of the j-th pollutant, and Y QT,j is the emission coefficient of the j-th pollutant when the virtual power plant purchases electricity from the power grid, and P GD,m (t) is the power purchase power of the power grid at time t on the m-th typical day, and P QT,m (t) is the distributed gas turbine power generation power at time t on the m-th typical day, Δt is the scheduling step length, Δt = 1, J is the type of pollutant, J = 3, which are carbon dioxide, sulfur dioxide, and nitrogen oxides respectively, and T is the total scheduling period, T = 24;

[0186] The calculation formula for the residual value income of the virtual power plant is as follows:

[0187]

[0188] In the formula, ξ is the proportion of the residual value income to the initial investment, γ is the discount rate, and L n is the service life of the n-th type of equipment.

[0189] The calculation formula for the power balance constraint of the virtual power plant is as follows:

[0190]

[0191] In the formula, P SD,m (t) is the power selling power at time t on the m-th typical day, and P WP,m (t) is the power of the wind turbine generator at time t on the m-th typical day, and P PV,m (t) is the power of the photovoltaic generator set at time t on the m-th typical day,QT,m The power of the gas turbine at time t of the m-th typical day is P(t). The energy storage discharge power at time t of the m-th typical day is PES_discharge(t). The energy storage charging power at time t of the m-th typical day is PES_charge(t), and VPPoutput is the total output power of the virtual power plant. P L,m The period load at time t of the m-th typical day is P(t). The upstream cycle load at time t of the m-th typical day is Pupstream(t). The downstream cycle load at time t of the m-th typical day is Pdownstream(t).

[0192] The calculation formula for the power constraint of the generator set is as follows:

[0193] θ W,P,m (t)β WP P WP,rated ≤P WP,m (t)≤θ W,P,m (t)P WP,rated

[0194] θ PV,m (t)β PV P PV,rated ≤P VP,m (t)≤θ PV,m (t)P VP,rated

[0195] In the formula, θ W,P,m (t) is the start-stop state of the wind turbine at time t of the m-th typical day, and β WP is the minimum load rate of the wind turbine, and P WP,rated is the rated capacity of the wind turbine, and θ PV,m (t) is the start-stop state of the photovoltaic generator at time t of the m-th typical day, and β PV is the minimum load rate of the photovoltaic generator, and P PV,rated is the rated capacity of the photovoltaic generator;

[0196] The calculation formula for the capacity constraint of the generator set is as follows:

[0197] P WP,min ≤P WP,m (t)≤ζP L (t)

[0198] P PV,min ≤P PV,m (t)≤ζP L (t)

[0199] In the formula, P WP,min is the minimum output power of the wind turbine, ζ is the simultaneous proportionality coefficient of the total load, and P L(t) is the time - period load at time t, P PV,min is the minimum output power of the photovoltaic power generation unit;

[0200] The calculation formula for the power constraint of the gas turbine is as follows:

[0201] 0 ≤ P QT,m (t) ≤ P QT,rated

[0202] In the formula, P QT,m (t) is the power generation power of the distributed gas turbine at time t of the m - th typical day, P QT,rated is the rated capacity of the gas turbine;

[0203] The calculation formula for the up - and - down ramp power constraint of the gas turbine is as follows:

[0204]

[0205] In the formula, is the down - ramp power limit value of the gas turbine, P QT,m (t - 1) is the power generation power of the distributed gas turbine at time t - 1 of the m - th typical day, is the up - ramp power limit value of the gas turbine;

[0206] The calculation formula for the equipment output constraint of the gas turbine is as follows:

[0207] θ QT,m (t)β QT P QT,rated ≤ P QT,m (t) ≤ θ QT,m (t)P QT,rated

[0208] In the formula, θ QT,m (t) is the start - stop state of the gas turbine at time t of the m - th typical day, β QT is the minimum load rate of the gas turbine;

[0209] The calculation formula for the capacity constraint of the gas turbine is as follows:

[0210]

[0211] In the formula, P QT,min is the minimum capacity of the gas turbine, is the maximum load;

[0212] The calculation formula for the energy storage balance constraint of the energy storage is as follows:

[0213]

[0214] In the formula, Q ESS,mThe energy storage capacity of the energy storage at time t of the m-th typical day is Q ESS,m The energy storage capacity of the energy storage at time t-1 of the m-th typical day is η ESS,in The charging efficiency of the energy storage is η ESS,out The discharging efficiency of the energy storage;

[0215] The calculation formula for the charging upper and lower limit constraints of the energy storage is as follows:

[0216] Q ESS,min P ESS,rated ≤Q ESS,m (t)≤Q ESS,max P ESS,rated

[0217] In the formula, Q ESS,min Is the minimum state limit value of the energy storage capacity, P ESS,rated Is the rated capacity of the energy storage, Q ESS,max Is the maximum state limit value of the energy storage capacity;

[0218] The calculation formula for the equal energy storage constraint at the start and end states of the energy storage is as follows:

[0219] Q ESS,m (0) = Q ESS,m (T - 1)

[0220] In the formula, Q ESS,m (0) is the energy storage capacity of the energy storage at the initial moment, Q ESS,m (T - 1) is the stored electricity of the energy storage in the last scheduling period of the scheduling cycle;

[0221] The calculation formula for the charging and discharging power constraints of the energy storage is as follows:

[0222]

[0223] In the formula, Is the charging state of the energy storage at time t of the m-th typical day, Is the minimum charging power of the energy storage, P ESS,in,m (t) is the charging capacity of the energy storage at time t of the m-th typical day, Is the maximum charging power of the energy storage, Is the discharging state of the energy storage at time t of the m-th typical day, Is the minimum discharging power of the energy storage, P ESS,out,m (t) is the discharging capacity of the energy storage at time t of the m-th typical day, Is the discharging state of the energy storage at time t of the m-th typical day, Is the maximum discharging power of the energy storage, γ ESS,in Is the charging rate of the energy storage, P ESS,rated Is the rated capacity of the energy storage, γESS,out is the discharge rate of the energy storage;

[0224] The calculation formula for the maximum discharge power constraint of the energy storage is as follows:

[0225]

[0226] In the formula, is the maximum load;

[0227] The calculation formula for the charge-discharge uniqueness constraint of the energy storage is as follows:

[0228]

[0229] The calculation formula for the power purchase and sale power constraint is as follows:

[0230]

[0231] The calculation formula for the power purchase / sale uniqueness constraint is as follows:

[0232] 0 ≤ θ GD,m (t) + θ SD,m (t) ≤ 1

[0233] In the formula, θ GD,m (t) is the state variable of the virtual power plant purchasing power from the main network at time t of the m-th typical day, is the minimum power limit for the virtual power plant to purchase power from the main network within time t, P GD,m (t) is the power of the virtual power plant purchasing power from the main network at time t of the m-th typical day, is the maximum power limit for the virtual power plant to purchase power from the main network within time t, θ SD,m (t) is the state variable of the virtual power plant selling power to the main network at time t of the m-th typical day, is the minimum power limit for the virtual power plant to sell power to the main network within time t, P SD,m (t) is the power of the virtual power plant selling power to the main network at time t of the m-th typical day, is the maximum power limit for the virtual power plant to sell power to the main network within time t.

[0234] It should be noted that the establishment process of the dynamic optimization trading model includes:

[0235] Constructing a second objective function with the minimum operating cost of the virtual power plant on a typical day as the goal;

[0236] Taking the power balance constraint of the virtual power plant, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the charging upper and lower limit constraint of the energy storage, the start and end state energy storage equality constraint of the energy storage, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage energy storage, the charge and discharge uniqueness constraint of the energy storage, the power purchase and sale power constraint, the power purchase / sale uniqueness constraint, and the demand response constraint as the constraint conditions, and combining with the second objective function to construct a virtual power plant capacity optimization model.

[0237] The calculation formula of the second objective function is as follows:

[0238] minF OP,m =(C YW,m +C ESS,m +C FUE,m +C QT,m +C GRID,m +C DR,m )

[0239] In the formula, F OP,m is the operating cost of the virtual power plant on the m-th typical day, and C DR,m is the daily demand response cost on the m-th typical day;

[0240] Among them, the daily demand response cost on the m-th typical day includes: the upstream load transfer compensation cost and the downstream load transfer compensation cost.

[0241] The demand response constraint includes: the transfer constraint of the transferable load, the transferable load balance constraint, the upstream / downstream uniqueness constraint;

[0242] The calculation formula of the transfer constraint of the transferable load is as follows:

[0243]

[0244] The calculation formula of the transferable load balance constraint is as follows:

[0245]

[0246] The calculation formula of the upstream / downstream uniqueness constraint is as follows:

[0247]

[0248] In the formula, is the state variable of the upstream load transfer at time t on the m-th typical day, and P L (t) is the load within time t, is the state variable of the downstream load transfer at time t of the m-th typical day, is the response ratio of the shiftable load.

[0249] In summary, a power resource scheduling optimization system for the electricity spot market proposed in this embodiment uses a two-stage optimization method to perform capacity planning and operation optimization on the virtual power plant, improving the stability and economy of the virtual power plant operation.

[0250] Embodiment III

[0251] To implement the above embodiment, the present disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in Embodiment I is implemented.

[0252] Embodiment IV

[0253] To implement the above embodiment, the present disclosure also proposes a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in Embodiment I is implemented.

[0254] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0255] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a manner that is not shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the embodiments of the present application.

[0256] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for optimizing power resource scheduling in a power spot market, characterized in that: The method comprises: Obtain the initial capacity of each distributed resource in the virtual power plant, the electricity price information for each period of a typical day, and the demand response cost for a typical day; Inputting the initial capacity of each distributed resource in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacity of each distributed resource in the virtual power plant; Inputting the electricity price information of each period of the typical day and the typical day demand response cost into a pre-established dynamic optimization transaction model to obtain the operation strategy of each distributed resource; Optimizing and scheduling each distributed resource based on an operation strategy of each distributed resource; The distributed resources in the virtual power plant include distributed power sources, energy storage and gas turbines; The distributed power source includes: a wind power generator set and a photovoltaic generator set.

2. The method according to claim 1, characterized in that The process of establishing the virtual power plant capacity optimization model includes: Constructing a first objective function with the goal of minimizing the annual economic cost of the virtual power plant; The virtual power plant power balance constraint, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the upper and lower limit constraints of the energy storage charging, the energy storage equal constraint of the starting and ending states, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage, the charge and discharge uniqueness constraint of the energy storage, the power purchase and sale power constraint, and the uniqueness constraint of the power purchase / sale are used as constraint conditions, and the virtual power plant capacity optimization model is constructed in combination with the first objective function.

3. The method according to claim 2, characterized in that The calculation formula of the first objective function is as follows: minF1=C INV +C OP +C ENV -R SY Where F1 is the annual economic cost of the virtual power plant, C INV is the equipment investment and construction cost of the virtual power plant, C OP is the annual operating cost of the virtual power plant, C ENV is the annual pollutant treatment cost of the virtual power plant, R SY is the residual value income of the virtual power plant; The calculation formula for the equipment investment and construction cost of the virtual power plant is as follows: In the formula, R INV,n is the annual conversion coefficient of the investment cost of the nth type of equipment, U n is the unit capacity investment cost of the nth type of equipment in the virtual power plant, P n,rated The capacity configured for the nth type of equipment, N is the number of types of aggregated equipment in the virtual power plant; The annual operating cost of the virtual power plant is calculated as follows: Where D m is the duration of the mth typical day in a year, C YW,m is the operation and maintenance cost of the generator set on the mth typical day, C ESS,m is the operation and maintenance cost of energy storage on the mth typical day, C FUE,m is the fuel cost of the gas turbine on the mth typical day, C QT,m is the start-up and shutdown cost of the gas turbine on the mth typical day, C GRID,m is the cost of interacting with the grid on the mth typical day, M is the typical day type, where M = 3, corresponding to summer, winter and transition seasons respectively; The calculation formula of the cost of interacting with the power grid on the mth typical day is as follows: In the formula, Q GD,m (t) is the electricity price purchased by the virtual power plant from the power grid at time t on the mth typical day, Q SD,m (t) is the electricity price sold by the virtual power plant to the grid at time t on the mth typical day, P SD,m (t) is the power sold by the power grid at time t on the mth typical day; The calculation formula for the annual pollutant treatment cost of the virtual power plant is as follows: In the formula, is the unit emission treatment cost of the jth pollutant, Y QT,j is the emission coefficient of the jth pollutant when the virtual power plant purchases electricity from the grid, P GD,m (t) is the power purchased by the power grid at time t on the mth typical day, P QT,m (t) is the power generation of the distributed gas turbine at time t on the mth typical day, Δt is the scheduling step, Δt=1, J is the type of pollutant, J=3, which are carbon dioxide, sulfur dioxide and nitrogen oxides respectively, T is the total scheduling period, T=24; The calculation formula of the residual value of the virtual power plant is as follows: In the formula, ξ is the ratio of residual value to initial investment, γ is the discount rate, and L n is the service life of the nth type of equipment.

4. The method according to claim 3, characterized in that The calculation formula of the virtual power plant power balance constraint is as follows: Where P SD,m (t) is the electricity sales power at time t of the mth typical day, P WP,m (t) is the power of the wind turbine generator set at time t on the mth typical day, P PV,m (t) is the power of the photovoltaic generator set at time t on the mth typical day, P QT,m (t) is the power of the gas turbine at time t on the mth typical day, is the energy storage discharge power at time t of the mth typical day, is the energy storage charging power at time t of the mth typical day, VPPoutput is the total output power of the virtual power plant, P L,m (t) is the load of the mth typical day at time t, is the upstream periodic load at time t of the mth typical day, is the downstream periodic load at time t of the mth typical day; The power constraint of the generator set is calculated as follows: θ W,P,m (t)β WP P WP,rated ≤P WP,m (t)≤θ W,P,m (t)P WP,rated θ PV,m (t)β PV P PV,rated ≤P VP,m (t)≤θ PV,m (t)P VP,rated In the formula, θ W,P,m (t) is the start / stop state of the wind turbine generator set at time t on the mth typical day, β WP is the minimum load rate of the wind turbine generator set, P WP,rated is the rated capacity of the wind turbine generator set, θ PV,m (t) is the start / stop state of the photovoltaic generator set at time t in the mth typical day, β PV is the minimum load rate of the photovoltaic generator set, P PV,rated is the rated capacity of the photovoltaic generator set; The calculation formula of the capacity constraint of the generator set is as follows: P WP,min ≤P WP,m (t)≤ζP L (t) P PV,min ≤P PV,m (t)≤ζP L (t) Where P WP,min is the minimum output power of the wind turbine generator set, ζ is the simultaneous proportional coefficient of the total load, P L (t) is the load of the period at time t, P PV,min is the minimum output power of the photovoltaic generator set; The calculation formula of the power constraint of the gas turbine is as follows: 0≤P QT,m (t)≤P QT,rated Where P QT,m (t) is the power generation of the distributed gas turbine at time t on the mth typical day, P QT,rated is the rated capacity of the gas turbine; The calculation formula for the up and down ramp power constraint of the gas turbine is as follows: In the formula, is the down-climb power limit value of the gas turbine, P QT,m (t-1) is the power generation of the distributed gas turbine at time t-1 on the mth typical day, is the climbing power limit value of the gas turbine; The calculation formula of the equipment output constraint of the gas turbine is as follows: θ QT,m (t)β QT P QT,rated ≤P QT,m (t)≤θ QT,m (t)P QT,rated In the formula, θ QT,m (t) is the start and stop state of the gas turbine at time t on the mth typical day, β QT is the minimum load rate of the gas turbine; The calculation formula of the capacity constraint of the gas turbine is as follows: Where P QT,min is the minimum capacity of the gas turbine, is the maximum load; The calculation formula of the energy storage balance constraint of the energy storage is as follows: In the formula, Q ESS,m (t) is the energy storage capacity of the mth typical day at time t, Q ESS,m (t-1) is the energy storage capacity of the mth typical day at time t-1, η ESS,in is the charging efficiency of energy storage, η ESS,out is the discharge efficiency of energy storage; The calculation formula for the upper and lower limit constraints of the energy storage charging is as follows: Q ESS,min P ESS,rated ≤Q ESS,m (t)≤Q ESS,max P eSS,rated In the formula, Q eSS,min is the minimum state limit value of energy storage capacity, P ESS,rated is the rated capacity of energy storage, Q ESS,max is the maximum state limit value of the energy storage capacity; The calculation formula for the energy storage equality constraint of the initial and final states of the energy storage is as follows: Q ESS,m (0)=Q ESS,m (T-1) In the formula, Q ESS,m (0) is the energy storage capacity at the initial moment, Q ESS,m (T-1) is the storage capacity of the energy storage in the last dispatch period of the dispatch cycle; The calculation formula of the charging and discharging power constraint of the energy storage is as follows: In the formula, is the charging state of the energy storage at time t of the mth typical day, is the minimum charging power of energy storage, P ESS,in,m (t) is the charging capacity of the energy storage at time t on the mth typical day, is the maximum charging power of the energy storage, is the discharge state of energy storage at time t of the mth typical day, is the minimum discharge power of energy storage, P ESS,out,m (t) is the discharge capacity of energy storage at time t on the mth typical day, is the discharge state of energy storage at time t of the mth typical day, is the maximum discharge power of energy storage, γ ESS,in is the charging rate of energy storage, P ESS,rated is the rated capacity of energy storage, γ ESS,out is the discharge rate of the stored energy; The calculation formula of the energy storage maximum discharge power constraint of the energy storage is as follows: In the formula, is the maximum load; The calculation formula for the uniqueness constraint of the energy storage charge and discharge is as follows: The calculation formula for the power purchase and sales power constraints is as follows: The calculation formula for the uniqueness constraint of power purchase / sale is as follows: 0≤θ GD,m (t)+θ SD,m (t)≤1 In the formula, θ GD,m (t) is the state variable of the virtual power plant purchasing electricity from the main grid at time t on the mth typical day, is the minimum power limit of the virtual power plant purchasing electricity from the main grid within time t, P GD,m (t) is the power purchased by the virtual power plant from the main grid at time t on the mth typical day, is the maximum power limit of the virtual power plant purchased from the main grid within time t, θ SD,m (t) is the state variable of the virtual power plant selling electricity to the main grid at time t on the mth typical day, is the minimum power limit of the virtual power plant selling electricity to the main grid within time t, P SD,m (t) is the power sold by the virtual power plant to the main grid at time t on the mth typical day, It is the maximum power limit of the virtual power plant selling electricity to the main grid within time t.

5. The method according to claim 4, characterized in that The process of establishing the dynamic optimization transaction model includes: Constructing a second objective function with the goal of minimizing the operating cost of the virtual power plant on a typical day; The virtual power plant power balance constraint, the power constraint of the generator set, the capacity constraint of the generator set, the power constraint of the gas turbine, the up and down climbing power constraint of the gas turbine, the equipment output constraint of the gas turbine, the capacity constraint of the gas turbine, the energy storage balance constraint of the energy storage, the upper and lower limit constraints of the energy storage charging, the energy storage equal constraint of the starting and ending states, the charge and discharge power constraint of the energy storage, the maximum discharge power constraint of the energy storage, the charge and discharge uniqueness constraint of the energy storage, the power purchase and sale power constraint, the uniqueness constraint of the power purchase / sale, and the demand response constraint are used as constraint conditions, and the virtual power plant capacity optimization model is constructed in combination with the second objective function.

6. The method according to claim 5, characterized in that The calculation formula of the second objective function is as follows: minF OP,m =(C YW,m +C ESS,m +C FUE,m +C QT,m +C GRID,m +C DR,m ) In the formula, F OP,m is the operating cost of the virtual power plant on the mth typical day, C DR,m is the daily demand response cost of the mth typical day; The daily demand response cost of the mth typical day includes: upstream load transfer compensation cost and downstream load transfer compensation cost.

7. The method according to claim 6, characterized in that The demand response constraints include: transfer constraints of transferable loads, transferable load balancing constraints, and upstream / downstream uniqueness constraints; The calculation formula of the transfer constraint of the transferable load is as follows: The calculation formula of the transferable load balancing constraint is as follows: The calculation formula for the upstream / downstream uniqueness constraint is as follows: In the formula, is the state variable of upstream load transfer at time t of the mth typical day, P L (t) is the load in time t, is the state variable of downstream load transfer at time t of the mth typical day, is the response proportion of the transferable load.

8. A power resource dispatch optimization system for the power spot market, characterized in that: The system comprises: An acquisition module is used to obtain the initial capacity of each distributed resource in the virtual power plant, the electricity price information of each period of a typical day, and the demand response cost of a typical day; A first optimization module, used for inputting the initial capacity of each distributed resource in the virtual power plant into a pre-established virtual power plant capacity optimization model to obtain the optimized capacity of each distributed resource in the virtual power plant; The second optimization module is used to input the electricity price information of each period of the typical day and the typical day demand response cost into a pre-established dynamic optimization transaction model to obtain the operation strategy of each distributed resource; A scheduling module, used for optimizing and scheduling the distributed resources based on the operation strategies of the distributed resources; The distributed resources in the virtual power plant include distributed power sources, energy storage and gas turbines; The distributed power source includes: a wind power generator set and a photovoltaic generator set.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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