Virtual power plant multi-stage bidding and optimal scheduling method based on electricity-to-heat characteristics

Abstract

The invention discloses an electricity-to-heat characteristic-based virtual power plant multi-stage bidding and optimal scheduling method, which comprises the following steps of: establishing a multi-stage excitation electricity price mechanism according to the peak-valley time period of an electric load and the output deviation of a new energy generator set, and setting an operation mechanism of a source-load double-side electricity-to-heat device and an energy storage device; comprehensively evaluating the adjustable capability of each power-to-heat device for assisting the novel virtual power plant to participate in the power market transaction; a multi-stage double-layer bidding model in which a novel virtual power plant participates in power market transaction bidding is established, the novel virtual power plant is taken as a leader, the maximum income is taken as a target, scheduling response priorities, response time periods, declaration prices and declaration electric quantity of a power-to-heat conversion device and an energy storage device are decided, and a power market transaction center is taken as a follower. With the aim of minimizing the electricity purchasing cost, the clearing electricity price and the clearing electricity quantity are decided; and establishing a novel virtual power plant multi-stage optimization scheduling model, and outputting a novel virtual power plant multi-stage optimization scheduling strategy.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and specifically relates to a multi-stage bidding and optimization scheduling method for virtual power plants based on electricity-to-heat characteristics. Background Art

[0002] With the rapid development of social economy, the shortage of traditional fossil energy and serious environmental pollution have become increasingly prominent. The vigorous development of clean energy has become an important direction for the development of virtual power plants. New energy represented by wind power and photovoltaic power is developing particularly rapidly. However, the output of new energy has random fluctuations, which will affect the stable operation of virtual power plants, and then lead to increased transaction costs when virtual power plants participate in electricity market transactions due to the deviation of new energy output.

[0003] At present, virtual power plants usually use flexible loads on the user side to improve the flexibility of operation in response to the impact of new energy output deviations. However, there are few studies on the flexible scheduling response mechanism on the source side of virtual power plants. Moreover, virtual power plants are no longer a single power supply. There are also cogeneration units, heating units and other equipment for combined heat and power supply, forming a combined heat and power virtual power plant. This will make it more complicated for virtual power plants to participate in electricity market transactions for bidding and optimized scheduling. How to effectively use the flexible resources on both the source and load sides for bidding and scheduling responses for virtual power plants, and how to use the flexibility of heating to assist virtual power plants in electricity trading and scheduling are issues that urgently need to be solved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a multi-stage bidding and optimization scheduling method for a virtual power plant based on electricity-to-heat characteristics.

[0005] In order to solve the above technical problems, the technical solution of the present invention is:

[0006] The present invention provides a multi-stage bidding and optimization scheduling method for a virtual power plant based on power-to-heat characteristics, comprising:

[0007] S1. On the basis of the original virtual power plant structure including new energy generating units, conventional generating units, conventional heating units, cogeneration units, electric loads, thermal loads, heat networks and power grids, different types of power-to-heat devices and energy storage devices are added on the source side and the load side respectively to form a new virtual power plant with power-to-heat conversion;

[0008] S2. Establish a multi-level incentive electricity price mechanism based on the peak, valley and normal periods of electricity load and the output deviation of new energy generators, and set up the operation mechanism of the power-to-heat device and energy storage device on both sides of the source and load;

[0009] S3. After aggregating resources of the power-to-heat devices on the source side and the load side, obtain the user behavior, basic data and operation data of each power-to-heat device on the load side, as well as the dynamic operation data and static attribute data of each power-to-heat device on the source side, and conduct a comprehensive assessment of the adjustable capacity of each power-to-heat device to assist the new virtual power plant in participating in power market transactions;

[0010] S4. Based on the operation mechanism of each heat-to-electricity device and energy storage device, the adjustable capacity of comprehensive evaluation, combined with the predicted change curves of electric load and heat load, and the change curves of new energy output, a multi-stage two-tier bidding model for the new virtual power plant to participate in the power market transaction bidding is established. The new virtual power plant is taken as the leader, with the goal of maximizing profits, to decide the dispatch response priority and response period, declared price and declared electricity quantity of the heat-to-electricity device and energy storage device; and the power market trading center is taken as the follower, with the goal of minimizing the purchase cost of electricity, to decide the clearing price and clearing quantity;

[0011] S5. According to the multi-stage bidding results of the new virtual power plant participating in the power market transaction bidding, set the optimization objective functions of different stages, establish a multi-stage optimization scheduling model for the new virtual power plant, and output the multi-stage optimization scheduling strategy for the new virtual power plant.

[0012] Further, in said S1, in said novel virtual power plant including power-to-heat conversion, the electric load is jointly borne by the power grid, new energy power generation units, conventional power generation units and cogeneration units, and the heat load is jointly borne by conventional heating units, cogeneration units, power-to-heat conversion devices on the source side and the load side, and energy storage devices;

[0013] The different types of electric heat conversion devices include air source heat pumps, ground source heat pumps, water source heat pumps, electric boilers, and electric heating equipment; the energy storage devices include electricity storage devices and heat storage devices. The electricity storage devices are used to store low-cost electricity or excess electricity to drive the electric heat conversion devices, and the heat storage devices are used to store excess heat generated after the electric heat conversion devices are operated. The selection and quantity configuration of the electric heat conversion devices are carried out according to the natural geographical conditions on the source side and the load side and the energy supply requirements.

[0014] Further, the S2 specifically includes:

[0015] During normal periods of electricity load, heat-to-electricity devices and energy storage devices can freely convert electricity to heat and charge and discharge energy; during peak periods of electricity load, energy-discharging electricity price incentives are given to encourage heat-to-electricity devices and energy storage devices to discharge energy, or by reducing the user's heat load, thereby reducing the power input during the operation of heat-to-electricity devices; during valley periods of electricity load, electricity price discounts are given to encourage heat-to-electricity devices and energy storage devices to convert electricity to heat and charge energy;

[0016] The output of the new energy generator set is predicted, and the deviation is calculated compared with the actual output value of the new energy generator set. The compensation price for the power grid electricity consumption of the power-to-heat device and the energy storage device is set according to the new energy output deviation. At the same time, by reducing the heat load supply of the virtual power plant, the power-to-heat device and the energy storage device on the source side and the load side are used to compensate for the heat load and increase the electric load supply of the virtual power plant, thereby compensating for the new energy output deviation.

[0017] Furthermore, the setting of the electricity compensation price for the electric heat conversion device and the energy storage device based on the new energy output deviation includes: when the deviation is within a first preset range, using a first compensation electricity price; when the deviation is within a second preset range, using a second compensation electricity price; when the deviation is within a third preset range, using a third compensation electricity price; the price order of the first compensation electricity price, the second compensation electricity price and the third compensation electricity price is: first compensation electricity price> second compensation electricity price> third compensation electricity price, indicating that the deviation corresponding to the first compensation electricity price is also larger.

[0018] Further, in S3, the user behavior of each electric heat conversion device on the load side includes the user's habit of using the electric heat conversion device, preferred time period, historical usage record, response speed when reducing the heat load or reducing the power required for the operation of the electric heat conversion device, adjustable range and adjustment period; the basic data of each electric heat conversion device on the load side includes the maximum power and minimum power of the electric heat conversion device; the operation data of each electric heat conversion device on the load side includes the start and stop time, energy consumption and operating power;

[0019] The dynamic operation data of each electric heat conversion device on the source side includes: the operation output, response speed, response time period and temperature change rate of the electric heat conversion device; the static attribute data of each electric heat conversion device on the source side includes the installation location, model, rated power, heating capacity and energy efficiency index;

[0020] The comprehensive evaluation of the adjustable capacity of each heat-to-electricity device to assist the new virtual power plant in participating in electricity market transactions includes: establishing an adjustable capacity model of each heat-to-electricity device based on the data of each heat-to-electricity device on both the source and load sides, and using the model to evaluate the adjustment range and response speed of each heat-to-electricity device under different time periods, different load conditions, and different electricity price fluctuations.

[0021] Furthermore, in S4, before establishing a multi-stage two-tier bidding model for a new type of virtual power plant to participate in power market transaction bidding, it includes:

[0022] According to the operation mechanism of each power-to-heat device and energy storage device, the adjustable capacity of comprehensive evaluation, combined with the predicted change curves of electric load and thermal load, and the change curves of new energy output, a mechanism is set for each power-to-heat device and energy storage device to participate in power market transactions as a flexible resource in the day-ahead, intra-day and real-time stages;

[0023] Among them, the day-ahead stage: before the day-ahead market transaction is opened, the new virtual power plant signs a bilateral contract with the power market trading center in the bilateral contract market, and the day-ahead transaction is opened at a preset time one day before the transaction day; the new virtual power plant predicts the power load of the electric heat conversion device and the output of the new energy power generation unit participating in the day-ahead dispatch in the day-ahead stage, formulates the corresponding new virtual power plant bidding strategy and reports it to the power market trading center; on the trading day, the new virtual power plant completes the day-ahead market transaction according to the reported bidding information; the day-ahead market transaction is closed at a preset time one day before the transaction day, and the power market trading center clears the day-ahead electricity price and electricity of the new virtual power plant after the transaction is closed;

[0024] Intraday stage: On the trading day, the power market trading center opens each trading session hour by hour. The new virtual power plant updates and re-forecasts the output of new energy generating units in real time during the intraday stage, and guides the relevant power-to-heat devices and energy storage devices to reduce heat load, release heat, reduce input power and reduce power load fluctuations according to the adjustable capacity of each power-to-heat device, the electricity price mechanism, and the operating mechanism of the power-to-heat devices and energy storage devices on both sides of the source and load. At the same time, the new virtual power plant re-formulates and reports the real-time market bidding strategy to the power market trading center hour by hour; During the intraday trading stage, the new virtual power plant completes the real-time market transaction according to the submitted bidding power. During the intraday stage, the power market trading center clears the intraday electricity price and power of the new virtual power plant after the real-time market transaction is completed;

[0025] Real-time stage: the balancing market is opened, and the new virtual power plant completes electricity trading based on the actual output of each heat-to-electricity device and energy storage device, and the actual output of other equipment. The power market trading center settles electricity transactions with the new virtual power plant based on the clearing electricity prices of the day-ahead market and the real-time market; after the real-time market transactions are completed, the balancing market is opened, and the deviation of the output of the new energy power generation units is absorbed through the heat-to-electricity devices, energy storage devices and other dispatchable equipment, and compensation fees required for balancing the electricity are charged to the new virtual power plant; if the actual electricity output of the new virtual power plant is greater than the planned output, the new virtual power plant sells the excess electricity at a price lower than the clearing price of the real-time market, otherwise it purchases the shortfall at a price higher than the clearing price of the real-time market.

[0026] Furthermore, in S4, the new virtual power plant acts as a leader and aims to maximize the profit, which is expressed as:

[0027]

[0028] F vpp The revenue of the new virtual power plant; B e,t B is the electricity sales revenue of the new virtual power plant participating in the power market transaction, which is related to the different transaction volumes and transaction prices of the new virtual power plant in the day-ahead, intraday and real-time stages; h,tis the heat sales revenue of the new virtual power plant; C re,t is the operating cost of the new energy generator set; C cg,t is the operating cost of conventional generator sets; C ch,t is the operating cost of the conventional heating unit; C chp,t is the operating cost of the combined heat and power unit; C eh,t is the operating cost of the electric heat transfer device when it is not used as a flexibility resource; C es,t is the operating cost of the energy storage device; C fr,t The compensation cost for the heat-to-electricity device as a flexible resource when it performs dispatch response in the day-ahead, intra-day and real-time stages is related to the compensation electricity price and response power corresponding to the dispatch response period; C buy The cost of purchasing the missing electricity;

[0029] The power market trading center acts as a follower and aims to minimize the power purchase cost, which can be expressed as:

[0030]

[0031] F emt is the electricity purchase cost of the power market trading center; b g,t is the electricity purchase price; P g,t For electricity purchase.

[0032] Furthermore, the multi-stage two-tier bidding model for the virtual power plant to participate in electricity market transaction bidding also includes setting bidding constraints, including: operating constraints of new energy power generation units, operating constraints of conventional power generation units, operating constraints of conventional heating units, operating constraints of cogeneration units, operating constraints of electric power conversion devices, response constraints of electric power conversion devices when performing dispatch response, electricity market transaction constraints, power balance constraints, and virtual power plant bidding electricity constraints.

[0033] Furthermore, in S5, the optimization objective functions of different stages are set, a multi-stage optimization scheduling model of a new virtual power plant is established, and a multi-stage optimization scheduling strategy of a new virtual power plant is output, which specifically includes:

[0034] The optimization objective function of the day-ahead dispatching stage is set: when the virtual power plant dispatches the electric heat storage device and the energy storage device as flexible resources, the upper-layer optimization dispatching model aims to optimize the electricity-to-heat cost and the charging and discharging cost, minimize the load fluctuation, and optimize the energy comfort, and outputs the remaining electric load after the day-ahead dispatching; the lower-layer optimization dispatching model aims to minimize the operating cost of the virtual power plant and the lowest carbon emissions, and outputs the output of each device dispatched by the virtual power plant on the day-ahead.

[0035] Set the optimization objective function for the intraday dispatch stage: The virtual power plant is based on the output of each device dispatched on the day before, with the goal of minimizing the output deviation adjustment cost of each device dispatched on the day before and on the day before, and outputs the output of each device dispatched on the day before;

[0036] Set the optimization objective function of the real-time scheduling stage: the virtual power plant is based on the output of each device scheduled during the day, with the goal of minimizing the deviation penalty cost of the virtual power plant from the plan, and outputs the output of each device in real-time scheduling of the virtual power plant.

[0037] Furthermore, the upper-level optimization scheduling model aims to optimize the electricity-to-heat cost and the charging and discharging cost, minimize the load fluctuation, and optimize the energy comfort, which can be expressed as:

[0038]

[0039] λ e,t P is the compensation electricity price for the time period t when the electric heat conversion device performs dispatch response; e,t,i is the operating power of the i-th electric heat conversion device for dispatch response in period t; β s,t is the operating unit price of the energy storage device; P s,t,i is the charging and discharging power of the i-th energy storage device; M is the number of electric heat conversion devices and energy storage devices;

[0040]

[0041] T is the scheduling period; L r,t To give priority to the use of new energy and the remaining electricity load after utilizing the flexible resources of power-to-heat devices;

[0042]

[0043] μ i,t is the proportion of the i-th electric heat conversion device and energy storage device as flexible resources to adjust the electric load at time t to the dispatching daily load; θ i,t P is a 0-1 variable for adjusting the dispatch response of the electric load. The value of 1 indicates that the ith electric heat conversion device and the energy storage device respond to the dispatch plan of the new virtual power plant at time t, and the value of 0 indicates that the ith electric heat conversion device and the energy storage device do not respond to the dispatch plan of the new virtual power plant at time t. i,t,max is the maximum response adjustment amount of the electric load of the ith electric heat conversion device and energy storage device at time t;

[0044] The goal is to minimize the output deviation adjustment cost of each device between day-ahead scheduling and intra-day scheduling, which is expressed as:

[0045]

[0046] ε j,tis the adjustment unit price of the jth adjustment device; P q,j,t , P n,j,t are the equipment outputs of the jth regulating equipment in day-ahead scheduling and intraday scheduling respectively; N is the number of participating regulating equipment;

[0047] The goal is to minimize the penalty cost of the virtual power plant's deviation from the plan, which can be expressed as:

[0048]

[0049] are the positive and negative deviation penalty coefficients respectively; ΔP f,t It is the deviation between the actual operating output of the virtual power plant and the planned output.

[0050] The beneficial effects of the present invention are:

[0051] (1) Based on the original virtual power plant structure including new energy power generation units, conventional power generation units, conventional heating units, cogeneration units, electric loads, thermal loads, heat networks and power grids, the present invention forms a new virtual power plant including electric heat conversion by adding different types of electric heat conversion devices and energy storage devices on the source side and the load side; by adding electric heat conversion devices and energy storage devices on both the source and load sides, the flexibility and economy of the operation of the virtual power plant are improved by utilizing the conversion characteristics of electric heat;

[0052] (2) The present invention establishes a multi-level incentive electricity price mechanism based on the peak, valley and normal periods of electricity load and the output deviation of new energy power generation units, and sets up an operation mechanism for the electric heat conversion devices and energy storage devices on both the source and load sides; after aggregating resources of the electric heat conversion devices on the source and load sides, the user behavior, basic data and operation data of each electric heat conversion device on the load side, as well as the dynamic operation data and static attribute data of each electric heat conversion device on the source side are obtained, and a comprehensive evaluation is conducted on the adjustable capacity of each electric heat conversion device to assist the new virtual power plant in participating in electricity market transactions; the electric heat conversion devices and energy storage devices can be used to shave the peak of electricity load and fill the valley, so that the virtual power plant can The power supply of the segment can be guaranteed, and considering the randomness of the output of new energy, a dynamic incentive electricity price that changes with the deviation can be set, which can ensure that the heat-to-electricity device and the energy storage device can better participate in the scheduling of the virtual power plant as flexible resources, make up for the deviation, and reduce the penalty cost of the virtual power plant due to insufficient electricity. In addition, the response characteristics of different types of heat-to-electricity devices and energy storage devices can be analyzed in a personalized way, and the adjustment range and response speed of the heat-to-electricity devices and energy storage devices under different time periods, different load conditions, and different electricity price fluctuations can be clarified, which is convenient for the coordinated scheduling of various heat-to-electricity devices and energy storage devices, and effectively exerts their respective response characteristics.

[0053] (3) The present invention establishes a multi-stage two-tier bidding model for a new type of virtual power plant to participate in power market transaction bidding based on the operating mechanism of each heat-to-electricity device and energy storage device, the adjustable capacity of comprehensive evaluation, and the predicted electric load and heat load change curves and the new energy output change curves. The new type of virtual power plant is used as a leader to maximize the profit and decide the dispatch response priority and response period, declared price and declared electricity quantity of the heat-to-electricity device and energy storage device. The power market trading center is used as a follower to minimize the purchase cost of electricity and decide the clearing price and clearing quantity. The bidding model for the virtual power plant to participate in different transaction stages can be established to realize the flexible scheduling of each heat-to-electricity device and energy storage device, so that the virtual power plant can sell more electricity during the high electricity price period, effectively improving the economic benefits of the virtual power plant.

[0054] (4) The present invention sets optimization objective functions for different stages based on the multi-stage bidding results of the new virtual power plant participating in the power market transaction bidding, establishes a multi-stage optimization scheduling model for the new virtual power plant, and outputs a multi-stage optimization scheduling strategy for the new virtual power plant; the virtual power plant can establish a multi-stage optimization scheduling model based on the multi-stage bidding results, obtain the optimal output of each equipment in different scheduling stages, ensure that the new virtual power plant can meet the bidding electricity in each transaction stage, and improve the economy, stability and environmental protection of the operation of the new virtual power plant.

[0055] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 This is a flow chart of a multi-stage bidding and optimization scheduling method for a virtual power plant based on power-to-heat characteristics of the present invention;

[0059] Figure 2 This is a schematic diagram of the structure of the novel virtual power plant of the present invention;

[0060] Figure 3It is a multi-stage bidding principle flow chart of the power market transaction of the present invention;

[0061] Figure 4 It is a schematic diagram of the bidding model of the novel virtual power plant and the electricity market trading center of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] like Figure 1 As shown, this embodiment 1 provides a multi-stage bidding and optimization scheduling method for a virtual power plant based on power-to-heat characteristics, including:

[0065] S1. On the basis of the original virtual power plant structure including new energy generating units, conventional generating units, conventional heating units, cogeneration units, electric loads, thermal loads, heat networks and power grids, different types of power-to-heat devices and energy storage devices are added on the source side and the load side respectively to form a new virtual power plant with power-to-heat conversion;

[0066] S2. Establish a multi-level incentive electricity price mechanism based on the peak, valley and normal periods of electricity load and the output deviation of new energy generators, and set up the operation mechanism of the power-to-heat device and energy storage device on both sides of the source and load;

[0067] S3. After aggregating resources of the power-to-heat devices on the source side and the load side, obtain the user behavior, basic data and operation data of each power-to-heat device on the load side, as well as the dynamic operation data and static attribute data of each power-to-heat device on the source side, and conduct a comprehensive assessment of the adjustable capacity of each power-to-heat device to assist the new virtual power plant in participating in power market transactions;

[0068] S4. Based on the operation mechanism of each heat-to-electricity device and energy storage device, the adjustable capacity of comprehensive evaluation, combined with the predicted change curves of electric load and heat load, and the change curves of new energy output, a multi-stage two-tier bidding model for the new virtual power plant to participate in the power market transaction bidding is established. The new virtual power plant is taken as the leader, with the goal of maximizing profits, to decide the dispatch response priority and response period, declared price and declared electricity quantity of the heat-to-electricity device and energy storage device; and the power market trading center is taken as the follower, with the goal of minimizing the purchase cost of electricity, to decide the clearing price and clearing quantity;

[0069] S5. According to the multi-stage bidding results of the new virtual power plant participating in the power market transaction bidding, set the optimization objective functions of different stages, establish a multi-stage optimization scheduling model for the new virtual power plant, and output the multi-stage optimization scheduling strategy for the new virtual power plant.

[0070] In this embodiment, in S1, the electric load of the new virtual power plant with power-to-heat conversion is jointly borne by the power grid, new energy generator sets, conventional generator sets and cogeneration units, and the heat load is jointly borne by conventional heating units, cogeneration units, power-to-heat devices on the source side and the load side, and energy storage devices;

[0071] The different types of electric heat conversion devices include air source heat pumps, ground source heat pumps, water source heat pumps, electric boilers, and electric heating equipment; the energy storage devices include electricity storage devices and heat storage devices. The electricity storage devices are used to store low-cost electricity or excess electricity to drive the electric heat conversion devices, and the heat storage devices are used to store excess heat generated after the electric heat conversion devices are operated. The selection and quantity configuration of the electric heat conversion devices are carried out according to the natural geographical conditions on the source side and the load side and the energy supply requirements.

[0072] like Figure 2 The new virtual power plant structure shown in the figure, after the new energy power generation unit produces green electricity, part of it is used as the electric energy input of the power-to-heat device on the source side, part of it is stored in the power storage device, and the excess can be sold to the power grid. The amount of green electricity input to the power storage device and the amount sold to the power grid will be determined based on the output of green electricity and the economic cost; conventional power generation units only provide electricity, and conventional heating units only provide heat, and the input of both is conventional fossil fuels; cogeneration units are used for power supply and heat supply, and the input is conventional fossil fuels; the power-to-heat device, power storage device and energy storage device added on the source side can enhance the flexible scheduling capability of power supply and heat supply on the source side as the flexibility resources on the source side, and the power-to-heat device The electric energy input consists of three parts. The first part is the green electricity output by the new energy power generation unit, the second part is the electric energy obtained from the power grid, and the third part is the electric energy supplied by the power storage device. Part of the electric energy stored in the power storage device is the low-priced electricity from the power grid, and the other part is green electricity. Part of the heat energy produced by the electric heat conversion device is used for heating the heating network, and the other part is stored in the heat storage device; part of the electric energy input of the electric heat conversion device on the load side is the electric energy purchased from the power grid, and the other part is the electric energy supplied by the power storage device. Part of the output heat energy is used for heating the heating network, and the other part is stored in the heat storage device. The power storage device is used to store low-priced electricity from the power grid and serves as the electric energy input of the electric heat conversion device. It should be noted that the electric heat conversion device, the electric storage device and the heat storage device on the source side and the load side

[0073] In this embodiment, S2 specifically includes:

[0074] During normal periods of electricity load, heat-to-electricity devices and energy storage devices can freely convert electricity to heat and charge and discharge energy; during peak periods of electricity load, energy-discharging electricity price incentives are given to encourage heat-to-electricity devices and energy storage devices to discharge energy, or by reducing the user's heat load, thereby reducing the power input during the operation of heat-to-electricity devices; during valley periods of electricity load, electricity price discounts are given to encourage heat-to-electricity devices and energy storage devices to convert electricity to heat and charge energy;

[0075] The output of the new energy generator set is predicted, and the deviation is calculated compared with the actual output value of the new energy generator set. The compensation price for the power grid electricity consumption of the power-to-heat device and the energy storage device is set according to the new energy output deviation. At the same time, by reducing the heat load supply of the virtual power plant, the power-to-heat device and the energy storage device on the source side and the load side are used to compensate for the heat load and increase the electric load supply of the virtual power plant, thereby compensating for the new energy output deviation.

[0076] It should be noted that, on the one hand, the use of electric heat conversion devices and energy storage devices to smooth the peak of the electric load and fill the valley enables the virtual power plant to ensure the power supply during the peak period of electric load. When smoothing the peak and filling the valley, the user's thermal load can be reduced. Here, it can be a separate reduction of the user's flexible thermal load, or it can be a reduction of the user's thermal load while using electric heat storage devices and heat storage devices to compensate for this part of the thermal load. Here, during the valley period of electric load, low electricity prices can be used to convert electricity to heat and store heat, and heat can be released during the peak period of electric load, or low electricity prices can be stored by energy storage devices during the valley period of electric load, and the stored low electricity prices can be used to drive the electric heat conversion device to supply heat during the peak period of electric load. This can indirectly increase the power supply of the virtual power plant. In addition, the electric heat conversion devices on the source side and the load side here The virtual power plant and energy storage devices do not execute unified dispatching control instructions. Some electric heat conversion devices and heat storage devices can convert electricity to heat and store heat during off-peak hours and release it during peak hours, while some electric heat conversion devices and heat storage devices are in a shutdown state. It is sufficient to reduce the flexible heat load alone. On the other hand, the output of new energy power generation units will be affected by the randomness of the weather, resulting in the deviation of the virtual power plant changing dynamically over time. If a single compensation electricity price is adopted, it will not be conducive to improving the operating efficiency of the virtual power plant, nor can it motivate the electric heat conversion devices and energy storage devices to flexibly respond to the dispatch to make up for the deviation. For example, when the deviation is small and a larger compensation price is used, the dispatching cost will increase. On the contrary, when the deviation is large and a smaller compensation price is used, it will not have an incentive effect, which will lead to an increase in the penalty cost of the virtual power plant.

[0077] In this embodiment, the electricity compensation price for the electric heat conversion device and the energy storage device is set according to the new energy output deviation, including: when the deviation is within a first preset range, a first compensation electricity price is adopted; when the deviation is within a second preset range, a second compensation electricity price is adopted; when the deviation is within a third preset range, a third compensation electricity price is adopted; the price order of the first compensation electricity price, the second compensation electricity price and the third compensation electricity price is: the first compensation electricity price> the second compensation electricity price> the third compensation electricity price, indicating that the deviation corresponding to the first compensation electricity price is also larger.

[0078] In actual applications, when the deviation is in the first preset range, it indicates that the output deviation of new energy is large, and more electric heat conversion devices and energy storage devices are needed to reduce the flexible heat load and the operating input power, and the flexible adjustment load range is larger, and the first compensation electricity price here is also larger; when the deviation is in the second preset range, it indicates that the output deviation of new energy is large, and some electric heat conversion devices and energy storage devices can be used for flexible adjustment, and the adjustable range is large, while some electric heat conversion devices and energy storage devices may not participate in the flexible adjustment and output according to the original scheduling plan; when the deviation is in the third preset range, it indicates that the output deviation of new energy is small, and a small number of electric heat conversion devices and energy storage devices need to be operated or the flexible heat load can be reduced alone to make up for the deviation. The flexible heat load here refers to the ability to reduce and transfer some heat load users.

[0079] In this embodiment, in S3, the user behavior of each electric heat conversion device on the load side includes the user's habit of using the electric heat conversion device, preferred time period, historical usage record, response speed when reducing the heat load or reducing the power required for the operation of the electric heat conversion device, adjustable range and adjustment period; the basic data of each electric heat conversion device on the load side includes the maximum power and minimum power of the electric heat conversion device; the operation data of each electric heat conversion device on the load side includes the start and stop time, energy consumption and operating power;

[0080] The dynamic operation data of each electric heat conversion device on the source side includes: the operation output, response speed, response time period and temperature change rate of the electric heat conversion device; the static attribute data of each electric heat conversion device on the source side includes the installation location, model, rated power, heating capacity and energy efficiency index;

[0081] The comprehensive evaluation of the adjustable capacity of each heat-to-electricity device to assist the new virtual power plant in participating in electricity market transactions includes: establishing an adjustable capacity model of each heat-to-electricity device based on the data of each heat-to-electricity device on both the source and load sides, and using the model to evaluate the adjustment range and response speed of each heat-to-electricity device under different time periods, different load conditions, and different electricity price fluctuations.

[0082] In actual applications, if the additional electric heat conversion devices and energy storage devices are all dispatched according to unified instructions, it will lead to a small peak of electric heat conversion and heat storage during the valley period of electric load, which is also unreasonable. Therefore, it is necessary to call them in different time periods according to the supply and demand of electric load and the response characteristics of each electric heat conversion device. For example, some user-side electric heat conversion devices and energy storage devices can be flexibly adjusted during the day because no one is at home during the day, and there is no need to consider the user's heating comfort too much. However, some user-side devices allow flexible adjustment during certain time periods, and the range of adjustment is relatively small. For the source-side electric heat conversion devices and energy storage devices, for example, when the source side is an air source heat pump unit, there will be multiple air source heat pump units used in parallel to form an air source heat pump cluster. Of course, there can also be multiple air source heat pump clusters. Then not all air source heat pumps are under the same control in every time period. The units can cooperate with each other, or there can be air source heat pump units and ground source heat pump units. However, different types of heat pump units have different operating characteristics and response capabilities, and personalized analysis is needed to clarify which electric heat conversion devices and energy storage devices can be flexibly dispatched in which time periods, and which dispatching has a higher priority.

[0083] like Figure 3 As shown, in this embodiment, in S4, before establishing a multi-stage two-tier bidding model for a new type of virtual power plant to participate in power market transaction bidding, it includes:

[0084] According to the operation mechanism of each power-to-heat device and energy storage device, the adjustable capacity of comprehensive evaluation, combined with the predicted change curves of electric load and thermal load, and the change curves of new energy output, a mechanism is set for each power-to-heat device and energy storage device to participate in power market transactions as a flexible resource in the day-ahead, intra-day and real-time stages;

[0085] Among them, the day-ahead stage: before the day-ahead market transaction is opened, the new virtual power plant signs a bilateral contract with the power market trading center in the bilateral contract market, and the day-ahead transaction is opened at a preset time one day before the transaction day; the new virtual power plant predicts the power load of the electric heat conversion device and the output of the new energy power generation unit participating in the day-ahead dispatch in the day-ahead stage, formulates the corresponding new virtual power plant bidding strategy and reports it to the power market trading center; on the trading day, the new virtual power plant completes the day-ahead market transaction according to the reported bidding information; the day-ahead market transaction is closed at a preset time one day before the transaction day, and the power market trading center clears the day-ahead electricity price and electricity of the new virtual power plant after the transaction is closed;

[0086] Intraday stage: On the trading day, the power market trading center opens each trading session hour by hour. The new virtual power plant updates and re-forecasts the output of new energy generating units in real time during the intraday stage, and guides the relevant power-to-heat devices and energy storage devices to reduce heat load, release heat, reduce input power and reduce power load fluctuations according to the adjustable capacity of each power-to-heat device, the electricity price mechanism, and the operating mechanism of the power-to-heat devices and energy storage devices on both sides of the source and load. At the same time, the new virtual power plant re-formulates and reports the real-time market bidding strategy to the power market trading center hour by hour; During the intraday trading stage, the new virtual power plant completes the real-time market transaction according to the submitted bidding power. During the intraday stage, the power market trading center clears the intraday electricity price and power of the new virtual power plant after the real-time market transaction is completed;

[0087] Real-time stage: the balancing market is opened, and the new virtual power plant completes electricity trading based on the actual output of each heat-to-electricity device and energy storage device, and the actual output of other equipment. The power market trading center settles electricity transactions with the new virtual power plant based on the clearing electricity prices of the day-ahead market and the real-time market; after the real-time market transactions are completed, the balancing market is opened, and the deviation of the output of the new energy power generation units is absorbed through the heat-to-electricity devices, energy storage devices and other dispatchable equipment, and compensation fees required for balancing the electricity are charged to the new virtual power plant; if the actual electricity output of the new virtual power plant is greater than the planned output, the new virtual power plant sells the excess electricity at a price lower than the clearing price of the real-time market, otherwise it purchases the shortfall at a price higher than the clearing price of the real-time market.

[0088] It should be noted that virtual power plants can aggregate the flexibility resources of power-to-heat devices and energy storage devices on both the source and load sides for coordinated optimization and control, reduce the fluctuations caused by the output of new energy power generation units, provide stability in the operation of virtual power plants and the competitiveness of virtual power plants in electricity market transactions. At present, the electricity market transactions that virtual power plants can participate in mainly include the day-ahead market, real-time market, bilateral contract market, balancing market and ancillary service market. When virtual power plants participate in multiple markets at the same time, and power-to-heat devices and energy storage devices use flexible resources for scheduling and response to participate in the operation of virtual power plants, the flexibility and economic benefits of virtual power plants will be effectively improved.

[0089] In bilateral open power market transactions, virtual power plants can participate in power market transactions as both power suppliers and power users, and transactions in the market are settled uniformly at the clearing price. Virtual power plants can optimize their bidding power and report appropriate power sales prices based on the predicted power market transaction prices to ensure that all bidding power is successful.

[0090] like Figure 4 As shown, in this embodiment, in S4, the new virtual power plant acts as a leader with the goal of maximizing revenue, which is expressed as:

[0091]

[0092] F vpp The revenue of the new virtual power plant; B e,t B is the electricity sales revenue of the new virtual power plant participating in the power market transaction, which is related to the different transaction volumes and transaction prices of the new virtual power plant in the day-ahead, intraday and real-time stages; h,t is the heat sales revenue of the new virtual power plant; C re,t is the operating cost of the new energy generator set; C cg,t is the operating cost of conventional generator sets; C ch,t is the operating cost of the conventional heating unit; C chp,t is the operating cost of the combined heat and power unit; C eh,t is the operating cost of the electric heat transfer device when it is not used as a flexibility resource; C es,t is the operating cost of the energy storage device; C fr,t The compensation cost for the heat-to-electricity device as a flexible resource when it performs dispatch response in the day-ahead, intra-day and real-time stages is related to the compensation electricity price and response power corresponding to the dispatch response period; C buy The cost of purchasing the missing electricity;

[0093] The power market trading center acts as a follower and aims to minimize the power purchase cost, which can be expressed as:

[0094]

[0095] F emt is the electricity purchase cost of the power market trading center; b g,t is the electricity purchase price; P g,t For electricity purchase.

[0096] It should be noted that the new virtual power plant, as the upper-level leader, declares the electricity volume and electricity price. Although its quotation mechanism and the quotation mechanism of other market participants do not directly interfere with the decision-making of the power market trading center, as a virtual power plant itself, it still needs to consider the declaration strategies of other market participants and the feedback of the power market trading center on the declaration strategy; when determining the power purchase plan, the power market trading center considers the declaration strategies of each bidding entity and makes a clearing decision with the goal of minimizing the power purchase cost after comprehensive analysis. Therefore, the game process of the new virtual power plant participating in the power market transaction bidding is regarded as a Stackelberg master-slave game.

[0097] In this embodiment, the multi-stage two-tier bidding model for the virtual power plant to participate in electricity market transaction bidding also includes setting bidding constraints, including: operating constraints of new energy power generation units, operating constraints of conventional power generation units, operating constraints of conventional heating units, operating constraints of cogeneration units, operating constraints of electric heat conversion devices, response constraints of electric heat conversion devices when performing scheduling responses, electricity market transaction constraints, power balance constraints, and virtual power plant bidding electricity constraints.

[0098] It should be noted that the bidding power constraint of the virtual power plant is expressed as:

[0099]

[0100] P vpp,t is the bidding power reported by the virtual power plant at time t; They are the upper and lower limits of the bidding electricity reported by the virtual power plant at time t;

[0101] The power market transaction constraints are expressed as:

[0102] (1-τ)Q BC,t ≤Q BC,t ≤(1+τ)Q BC,t ;

[0103] Q BC,t is the trading electricity quantity stipulated in the contract during period t; τ is the deviation coefficient in the contract that allows a certain deviation between the actual trading electricity quantity and the electricity quantity stipulated in the contract.

[0104] In actual applications, for the two-layer bidding model, the upper-layer new virtual power plant bidding model is solved by using an intelligent optimization algorithm, and the lower-layer calls the Cplex solver for solution.

[0105] In this embodiment, in S5, the optimization objective functions of different stages are set, a new virtual power plant multi-stage optimization scheduling model is established, and a new virtual power plant multi-stage optimization scheduling strategy is output, which specifically includes:

[0106] The optimization objective function of the day-ahead dispatching stage is set: when the virtual power plant dispatches the electric heat storage device and the energy storage device as flexible resources, the upper-layer optimization dispatching model aims to optimize the electricity-to-heat cost and the charging and discharging cost, minimize the load fluctuation, and optimize the energy comfort, and outputs the remaining electric load after the day-ahead dispatching; the lower-layer optimization dispatching model aims to minimize the operating cost of the virtual power plant and the lowest carbon emissions, and outputs the output of each device dispatched by the virtual power plant on the day-ahead.

[0107] Set the optimization objective function for the intraday dispatch stage: The virtual power plant is based on the output of each device dispatched on the day before, with the goal of minimizing the output deviation adjustment cost of each device dispatched on the day before and on the day before, and outputs the output of each device dispatched on the day before;

[0108] Set the optimization objective function of the real-time scheduling stage: the virtual power plant is based on the output of each device scheduled during the day, with the goal of minimizing the deviation penalty cost of the virtual power plant from the plan, and outputs the output of each device in real-time scheduling of the virtual power plant.

[0109] In this embodiment, the upper-level optimization scheduling model aims to optimize the electricity-to-heat cost and the charging and discharging cost, minimize the load fluctuation, and optimize the energy comfort, which is expressed as:

[0110]

[0111] λ e,t P is the compensation electricity price for the time period t when the electric heat conversion device performs dispatch response; e,t,i is the operating power of the i-th electric heat conversion device for dispatch response in period t; β s,t is the operating unit price of the energy storage device; P s,t,i is the charging and discharging power of the i-th energy storage device; M is the number of electric heat conversion devices and energy storage devices;

[0112]

[0113] T is the scheduling period; L r,t To give priority to the use of new energy and the remaining electricity load after utilizing the flexible resources of power-to-heat devices;

[0114]

[0115] μ i,t is the proportion of the i-th electric heat conversion device and energy storage device as flexible resources to adjust the electric load at time t to the dispatching daily load; θ i,t P is a 0-1 variable for adjusting the dispatch response of the electric load. The value of 1 indicates that the ith electric heat conversion device and the energy storage device respond to the dispatch plan of the new virtual power plant at time t, and the value of 0 indicates that the ith electric heat conversion device and the energy storage device do not respond to the dispatch plan of the new virtual power plant at time t. i,t,max is the maximum response adjustment amount of the electric load of the ith electric heat conversion device and energy storage device at time t;

[0116] The goal is to minimize the output deviation adjustment cost of each device between day-ahead scheduling and intra-day scheduling, which is expressed as:

[0117]

[0118] ε j,t is the adjustment unit price of the jth adjustment device; P q,j,t , P n,j,t are the equipment outputs of the jth regulating equipment in day-ahead scheduling and intraday scheduling respectively; N is the number of participating regulating equipment;

[0119] The goal is to minimize the deviation penalty cost of the virtual power plant from the plan, which can be expressed as:

[0120]

[0121] are the positive and negative deviation penalty coefficients respectively; ΔP f,t It is the deviation between the actual operating output of the virtual power plant and the planned output.

[0122] In several embodiments provided in the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] In addition, the functional modules in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

[0124] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A multi-stage bidding and optimization scheduling method for a virtual power plant based on power-to-heat characteristics, characterized in that: include: S1. On the basis of the original virtual power plant structure including new energy generating units, conventional generating units, conventional heating units, cogeneration units, electric loads, thermal loads, heat networks and power grids, different types of power-to-heat devices and energy storage devices are added on the source side and the load side respectively to form a new virtual power plant with power-to-heat conversion; S2. Establish a multi-level incentive electricity price mechanism based on the peak, valley and normal periods of electricity load and the output deviation of new energy generators, and set up the operation mechanism of the power-to-heat device and energy storage device on both sides of the source and load; S3. After aggregating resources of the power-to-heat devices on the source side and the load side, obtain the user behavior, basic data and operation data of each power-to-heat device on the load side, as well as the dynamic operation data and static attribute data of each power-to-heat device on the source side, and conduct a comprehensive assessment of the adjustable capacity of each power-to-heat device to assist the new virtual power plant in participating in power market transactions; S4. Based on the operation mechanism of each heat-to-electricity device and energy storage device, the adjustable capacity of comprehensive evaluation, combined with the predicted change curves of electric load and heat load, and the change curves of new energy output, a multi-stage two-tier bidding model for the new virtual power plant to participate in the power market transaction bidding is established. The new virtual power plant is taken as the leader, with the goal of maximizing profits, to decide the dispatch response priority and response period, declared price and declared electricity quantity of the heat-to-electricity device and energy storage device; and the power market trading center is taken as the follower, with the goal of minimizing the purchase cost of electricity, to decide the clearing price and clearing quantity; S5. According to the multi-stage bidding results of the new virtual power plant participating in the power market transaction bidding, set the optimization objective functions of different stages, establish a multi-stage optimization scheduling model for the new virtual power plant, and output the multi-stage optimization scheduling strategy for the new virtual power plant.

2. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 1 is characterized in that: In S1, the electric load of the new virtual power plant with electricity-to-heat conversion is jointly borne by the power grid, new energy generator sets, conventional generator sets and cogeneration units, and the heat load is jointly borne by conventional heating units, cogeneration units, electricity-to-heat devices on the source side and the load side, and energy storage devices; The different types of electric heat conversion devices include air source heat pumps, ground source heat pumps, water source heat pumps, electric boilers, and electric heating equipment; the energy storage devices include electricity storage devices and heat storage devices. The electricity storage devices are used to store low-cost electricity or excess electricity to drive the electric heat conversion devices, and the heat storage devices are used to store excess heat generated after the electric heat conversion devices are operated. The selection and quantity configuration of the electric heat conversion devices are carried out according to the natural geographical conditions on the source side and the load side and the energy supply requirements.

3. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 1 is characterized in that: The S2 specifically includes: During normal periods of electricity load, heat-to-electricity devices and energy storage devices can freely convert electricity to heat and charge and discharge energy; during peak periods of electricity load, energy-discharging electricity price incentives are given to encourage heat-to-electricity devices and energy storage devices to discharge energy, or by reducing the user's heat load, thereby reducing the power input during the operation of heat-to-electricity devices; during valley periods of electricity load, electricity price discounts are given to encourage heat-to-electricity devices and energy storage devices to convert electricity to heat and charge energy; The output of the new energy generator set is predicted, and the deviation is calculated compared with the actual output value of the new energy generator set. The compensation price for the power grid electricity consumption of the power-to-heat device and the energy storage device is set according to the new energy output deviation. At the same time, by reducing the heat load supply of the virtual power plant, the power-to-heat device and the energy storage device on the source side and the load side are used to compensate for the heat load and increase the electric load supply of the virtual power plant, thereby compensating for the new energy output deviation.

4. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 3 is characterized in that: The method of setting the electricity compensation price for the electric heat conversion device and the energy storage device based on the new energy output deviation includes: when the deviation is within a first preset range, using a first compensation electricity price; when the deviation is within a second preset range, using a second compensation electricity price; when the deviation is within a third preset range, using a third compensation electricity price; the price sequence of the first compensation electricity price, the second compensation electricity price and the third compensation electricity price is: first compensation electricity price> second compensation electricity price> third compensation electricity price, indicating that the deviation corresponding to the first compensation electricity price is also larger.

5. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 1 is characterized in that: In S3, the user behavior of each electric heat conversion device on the load side includes the user's habit of using the electric heat conversion device, preferred time period, historical usage record, response speed when reducing the heat load or reducing the power required for the operation of the electric heat conversion device, adjustable range and adjustment period; the basic data of each electric heat conversion device on the load side includes the maximum power and minimum power of the electric heat conversion device; the operation data of each electric heat conversion device on the load side includes the start and stop time, energy consumption and operating power; The dynamic operation data of each electric heat conversion device on the source side includes: the operation output, response speed, response time period and temperature change rate of the electric heat conversion device; the static attribute data of each electric heat conversion device on the source side includes the installation location, model, rated power, heating capacity and energy efficiency index; The comprehensive evaluation of the adjustable capacity of each heat-to-electricity device to assist the new virtual power plant in participating in electricity market transactions includes: establishing an adjustable capacity model of each heat-to-electricity device based on the data of each heat-to-electricity device on both the source and load sides, and using the model to evaluate the adjustment range and response speed of each heat-to-electricity device under different time periods, different load conditions, and different electricity price fluctuations.

6. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 1 is characterized in that: In S4, before establishing a multi-stage two-tier bidding model for a new type of virtual power plant to participate in power market transaction bidding, it includes: According to the operation mechanism of each power-to-heat device and energy storage device, the adjustable capacity of comprehensive evaluation, combined with the predicted change curves of electric load and thermal load, and the change curves of new energy output, a mechanism is set for each power-to-heat device and energy storage device to participate in power market transactions as a flexible resource in the day-ahead, intra-day and real-time stages; Among them, the day-ahead stage: before the day-ahead market transaction is opened, the new virtual power plant signs a bilateral contract with the power market trading center in the bilateral contract market, and the day-ahead transaction is opened at a preset time one day before the transaction day; the new virtual power plant predicts the power load of the electric heat conversion device and the output of the new energy power generation unit participating in the day-ahead dispatch in the day-ahead stage, formulates the corresponding new virtual power plant bidding strategy and reports it to the power market trading center; on the trading day, the new virtual power plant completes the day-ahead market transaction according to the reported bidding information; the day-ahead market transaction is closed at a preset time one day before the transaction day, and the power market trading center clears the day-ahead electricity price and electricity of the new virtual power plant after the transaction is closed; Intraday stage: On the trading day, the power market trading center opens each trading session hour by hour. The new virtual power plant updates and re-forecasts the output of new energy generating units in real time during the intraday stage, and guides the relevant power-to-heat devices and energy storage devices to reduce heat load, release heat, reduce input power and reduce power load fluctuations according to the adjustable capacity of each power-to-heat device, the electricity price mechanism, and the operating mechanism of the power-to-heat devices and energy storage devices on both sides of the source and load. At the same time, the new virtual power plant re-formulates and reports the real-time market bidding strategy to the power market trading center hour by hour; During the intraday trading stage, the new virtual power plant completes the real-time market transaction according to the submitted bidding power. During the intraday stage, the power market trading center clears the intraday electricity price and power of the new virtual power plant after the real-time market transaction is completed; Real-time stage: the balancing market is opened, and the new virtual power plant completes electricity trading based on the actual output of each heat-to-electricity device and energy storage device, and the actual output of other equipment. The power market trading center settles electricity transactions with the new virtual power plant based on the clearing electricity prices of the day-ahead market and the real-time market; after the real-time market transactions are completed, the balancing market is opened, and the deviation of the output of the new energy power generation units is absorbed through the heat-to-electricity devices, energy storage devices and other dispatchable equipment, and compensation fees required for balancing the electricity are charged to the new virtual power plant; if the actual electricity output of the new virtual power plant is greater than the planned output, the new virtual power plant sells the excess electricity at a price lower than the clearing price of the real-time market, otherwise it purchases the shortfall at a price higher than the clearing price of the real-time market.

7. The multi-stage bidding and optimization scheduling method for a virtual power plant according to claim 1 is characterized in that: In S4, the new virtual power plant acts as a leader and aims to maximize revenue, which is expressed as: F vpp The revenue of the new virtual power plant; B e,t B is the electricity sales revenue of the new virtual power plant participating in the power market transaction, which is related to the different transaction volumes and transaction prices of the new virtual power plant in the day-ahead, intraday and real-time stages; h,t is the heat sales revenue of the new virtual power plant; C re,t is the operating cost of the new energy generator set; C cg,t is the operating cost of conventional generator sets; C ch,t is the operating cost of the conventional heating unit; C chp,t is the operating cost of the combined heat and power unit; C eh,t is the operating cost of the electric heat transfer device when it is not used as a flexibility resource; C es,t is the operating cost of the energy storage device; C fr,t The compensation cost for the heat-to-electricity device as a flexible resource when it performs dispatch response in the day-ahead, intra-day and real-time stages is related to the compensation electricity price and response power corresponding to the dispatch response period; C buy The cost of purchasing the insufficient electricity; The power market trading center acts as a follower and aims to minimize the power purchase cost, which can be expressed as: F emt is the electricity purchase cost of the power market trading center; b g,t is the electricity purchase price; P g,t For electricity purchase.

8. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 1 is characterized in that: The multi-stage two-tier bidding model for the virtual power plant to participate in electricity market transaction bidding also includes setting bidding constraints, including: operating constraints of new energy power generation units, operating constraints of conventional power generation units, operating constraints of conventional heating units, operating constraints of cogeneration units, operating constraints of electric heat conversion devices, response constraints of electric heat conversion devices when performing dispatch response, electricity market transaction constraints, power balance constraints, and virtual power plant bidding electricity constraints.

9. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 1 is characterized in that: In S5, the optimization objective functions of different stages are set, a new virtual power plant multi-stage optimization scheduling model is established, and a new virtual power plant multi-stage optimization scheduling strategy is output, which specifically includes: The optimization objective function of the day-ahead dispatching stage is set: when the virtual power plant dispatches the electric heat storage device and the energy storage device as flexible resources, the upper-layer optimization dispatching model aims to optimize the electricity-to-heat cost and the charging and discharging cost, minimize the load fluctuation, and optimize the energy comfort, and outputs the remaining electric load after the day-ahead dispatching; the lower-layer optimization dispatching model aims to minimize the operating cost of the virtual power plant and the lowest carbon emissions, and outputs the output of each device dispatched by the virtual power plant on the day-ahead. Set the optimization objective function for the intraday dispatch stage: The virtual power plant is based on the output of each device dispatched on the day before, with the goal of minimizing the output deviation adjustment cost of each device dispatched on the day before and on the day before, and outputs the output of each device dispatched on the day before; Set the optimization objective function of the real-time scheduling stage: the virtual power plant is based on the output of each device scheduled during the day, with the goal of minimizing the deviation penalty cost of the virtual power plant from the plan, and outputs the output of each device in real-time scheduling of the virtual power plant.

10. The multi-stage bidding and optimization scheduling method for virtual power plants according to claim 9 is characterized in that: The upper-level optimization scheduling model aims to optimize the electricity-to-heat cost and charging and discharging cost, minimize load fluctuation, and optimize energy comfort, which can be expressed as: λ e,t P is the compensation electricity price for the time period t when the electric heat conversion device performs dispatch response; e,t,i is the operating power of the i-th electric heat conversion device for dispatch response in period t; β s,t is the operating unit price of the energy storage device; P s,t,i is the charging and discharging power of the i-th energy storage device; M is the number of electric heat conversion devices and energy storage devices; T is the scheduling period; L r,t To give priority to the use of new energy and the remaining electricity load after utilizing the flexible resources of power-to-heat devices; μ i,t is the proportion of the i-th electric heat conversion device and energy storage device as flexible resources to adjust the electric load at time t to the dispatching daily load; θ i,t P is a 0-1 variable for adjusting the dispatch response of the electric load. The value of 1 indicates that the ith electric heat conversion device and the energy storage device respond to the dispatch plan of the new virtual power plant at time t, and the value of 0 indicates that the ith electric heat conversion device and the energy storage device do not respond to the dispatch plan of the new virtual power plant at time t. i,t,max is the maximum response adjustment amount of the electric load of the ith electric heat conversion device and energy storage device at time t; The goal is to minimize the output deviation adjustment cost of each device between day-ahead scheduling and intra-day scheduling, which is expressed as: ε j,t is the adjustment unit price of the jth adjustment device; P q,j,t , P n,j,t are the equipment outputs of the jth regulating equipment in day-ahead scheduling and intraday scheduling respectively; N is the number of participating regulating equipment; The goal is to minimize the deviation penalty cost of the virtual power plant from the plan, which can be expressed as: are the positive and negative deviation penalty coefficients respectively; ΔP f,t It is the deviation between the actual operating output of the virtual power plant and the planned output.

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