Method and device for virtual power plant to participate in market transaction

Abstract

The invention provides a method and device for a virtual power plant to participate in market transaction, and the method comprises the steps: solving a target response amount by taking a minimum cost function as an optimization target after constructing the virtual power plant, determining a target excitation compensation price based on the target response amount and the minimum cost function value, and then taking a maximum preset benefit function as an optimization target, based on the target response amount and the target excitation compensation price, the target electricity purchase and sale amount and the target peak load shifting amount are solved, the electricity utilization cost of the virtual power plant is minimized, the economic benefit of the virtual power plant is maximized, the electricity utilization experience of a user is improved, and in addition, the target response amount and the target excitation compensation electricity price are obtained through the minimum electricity utilization cost. The enthusiasm of the user to change the power demand is improved, the power shortage condition is relieved, the abnormal power utilization condition caused by the power shortage is avoided, and the power utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and in particular to a method and device for a virtual power plant to participate in market transactions. Background Art

[0002] At present, the installed capacity ratio of new energy power stations is gradually increasing, while the randomness, volatility and intermittency of the output of new energy power stations lead to uncertainty in the output on the power supply side; on the load side, with the continuous increase of new energy vehicles, the installed capacity of high-power charging piles is also increasing, resulting in great uncertainty in the energy consumption on the load side; in order to achieve the energy balance between source and load, the virtual power plant organizes the power supply side, load side and energy storage to participate in the electricity trading market, thereby realizing the interaction between source, load and storage and achieving the goal of energy balance.

[0003] As an aggregate of distributed resources, virtual power plants can only improve their electricity efficiency by participating in the electricity market with an optimal trading strategy, which is crucial to their efficient development. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for a virtual power plant to participate in market transactions, so as to improve the power efficiency of the virtual power plant and further improve the power efficiency of the load.

[0005] According to one aspect of the present invention, a method for a virtual power plant to participate in market transactions is provided, the method being applied to a virtual power plant, the virtual power plant comprising a power supply side, an energy storage side and a load side, the load side comprising an adjustable load and a transferable load, the method comprising:

[0006] Obtaining preset parameters of each component of the virtual power plant, wherein the components include a power supply side, an energy storage side, and a load side;

[0007] Taking minimization of a preset cost function as the optimization goal, the target response quantity is solved by using a Cplex solver combined with a particle swarm algorithm, wherein the preset cost function includes the response quantity, the incentive compensation price, and the electricity price of the virtual power plant;

[0008] Based on the target response amount and the minimum value of the preset cost function, obtaining a target incentive compensation price;

[0009] Taking maximizing the preset benefit function as the optimization goal, using the Cplex solver, based on the target response amount and the target incentive compensation price, the target purchase and sale power amount and the target peak shaving and valley filling amount are solved, wherein the preset benefit function includes the response amount, the incentive compensation price, the purchase and sale power amount and the peak shaving and valley filling amount, and the peak shaving and valley filling amount includes the power consumption reduced by the load side during the preset peak power consumption period and the power consumption increased during the preset valley power consumption period;

[0010] Determine whether the target power purchase and sale amount and the target peak shaving and valley filling amount meet the preset constraints. If not, return to the step of minimizing the preset cost function as the optimization goal, and use the Cplex solver combined with the particle swarm algorithm to solve the target response amount. If they meet the requirements, determine the target incentive electricity price, the target power purchase and sale amount, and the target peak shaving and valley filling amount as the target strategy for the virtual power plant to participate in market transactions.

[0011] In a possible embodiment, the virtual power plant further includes a power management system, an energy storage management system, and a load management system, and obtaining preset parameters of each component of the virtual power plant includes:

[0012] Access the power management system, energy storage management system and load management system, and obtain preset parameters of each component of the virtual power plant according to a preset data acquisition method, wherein the preset data acquisition method includes: obtaining through an interface and obtaining through a database.

[0013] In a possible embodiment, the preset cost function includes a cost function of an adjustable load and a cost function of a transferable load, wherein the cost function of the adjustable load is:

[0014]

[0015] in, is the electricity cost of the adjustable load, is the electricity price of the virtual power plant, is the response of the adjustable load, σ and υ are the unsatisfactory cost coefficients, is the reduction amount of the adjustable load, is the incentive compensation price set by the virtual power plant for the adjustable load user in the ε electricity consumption scenario;

[0016] The cost function of the transferable load is:

[0017]

[0018] in, is the electricity cost of the transferable load, is the electricity price of the virtual power plant, is the response amount of the transferable load, is the transfer-out amount of the transferable load, is the incentive compensation price set by the virtual power plant for the transferable load user in the ε electricity consumption scenario, When it is 1, it indicates that there is load transfer in the ε power consumption scenario;

[0019] The electricity cost function is:

[0020]

[0021] Among them, C load is the electricity cost on the load side, N ε is the total number of electricity consumption scenarios, η ε is the preset coefficient.

[0022] In a possible embodiment, minimizing a preset cost function as an optimization goal and using a Cplex solver in combination with a particle swarm algorithm to solve a target response amount include:

[0023] Setting the number of iterations, the number of particles, and the convergence error in the Cplex solver, and initializing the incentive compensation price particles;

[0024] Using the Cplex solver to update the speed and position of each of the incentive compensation price particles, and find the minimum value of the preset cost function;

[0025] When the minimum value of the preset cost function is obtained, the corresponding target response amount is determined using the Cplex.

[0026] In a possible embodiment, the method further includes:

[0027] Determine whether the incentive compensation price particle and the target response amount meet the preset first constraint condition. If not, return to the step of using the Cplex solver to update the speed and position of each incentive compensation price particle and find the minimum value of the preset cost function, wherein the first constraint condition includes: a reduction condition and a transfer condition, wherein the reduction condition is:

[0028]

[0029] in, is a preset maximum value of the load that can be curtailed, wherein the maximum value of the load that can be curtailed is obtained based on the maximum power consumption of the adjustable load participating in the demand response;

[0030] The transfer conditions are:

[0031]

[0032] in, and is the upper limit of the transferable in and out values ​​of the transferable load at time t, It means that the sum of electricity consumption in each electricity consumption scenario is the same at time t.

[0033] In a possible embodiment, the preset benefit function is:

[0034]

[0035] Among them, P is the economic benefit, ω represents the electricity usage scenario, and N represents the number of electricity usage scenarios. represents the revenue of the virtual power plant in the energy market, represents the revenue of the virtual power plant in the peak load market, represents the revenue obtained by the load side, C ω Represents the operating cost of the virtual power plant, wherein the income of the virtual power plant in the energy market is positively correlated with the incentive compensation electricity price and the electricity sales amount of the virtual power plant, and negatively correlated with the electricity purchase amount; the income of the virtual power plant during the peak-shaving period is positively correlated with the peak-shaving and valley-filling amount, the incentive compensation electricity price during the peak electricity consumption period, and the incentive compensation electricity price during the valley electricity consumption period; the income obtained by the load side is positively correlated with the response amount and the incentive compensation price.

[0036] In a possible embodiment, the preset constraints include: market transaction condition constraints, energy storage constraints, diesel unit constraints, and participant balance constraints, wherein the market transaction condition constraints are:

[0037]

[0038] in, is the electricity sales volume of the virtual power plant under the electricity consumption scenario ε at time t, is the amount of electricity purchased by the virtual power plant under the electricity consumption scenario ε at time t, is the maximum amount of saleable electricity in the virtual power plant at time t, is the maximum amount of electricity that can be purchased in the virtual power plant at time t;

[0039] The energy storage constraint is:

[0040]

[0041] in, as well as They respectively represent the charge and discharge amounts of the energy storage side at time t, When it is 1, it indicates that the energy storage side is in charging state. When it is 1, it indicates that the energy storage side is in a discharging state. as well as Respectively represent the maximum value of charging power and the maximum value of discharging power on the energy storage side;

[0042] The diesel group constraints are:

[0043]

[0044] in, as well as are the minimum and maximum output of the diesel generator set, is the output of the diesel generator set at time t;

[0045]

[0046] in, is the output of the diesel generator set at time t, is the energy storage side discharge at time t, To charge the energy storage side at time t, is the power consumption of the adjustable load at time t, is the transferable load power consumption at time t, is the electricity sales of the virtual power plant at time t, is the amount of electricity purchased by the virtual power plant at time t.

[0047] According to another aspect of the present invention, a device for a virtual power plant to participate in market transactions is provided, the device being applied to a virtual power plant, the virtual power plant comprising a power supply side, an energy storage side and a load side, the load side comprising an adjustable load and a transferable load, the device comprising:

[0048] An acquisition module, used to acquire preset parameters of various components of the virtual power plant, wherein the components include a power supply side, an energy storage side, and a load side;

[0049] A first solving module is used to solve the target response quantity by minimizing a preset cost function as an optimization goal and using a Cplex solver combined with a particle swarm algorithm, wherein the preset cost function includes the response quantity, the incentive compensation price, and the electricity price of the virtual power plant;

[0050] A calculation module, used to obtain a target incentive compensation price based on the target response amount and a minimum value of the preset cost function;

[0051] The second solving module is used to maximize the preset benefit function as the optimization goal, and use the Cplex solver to solve the target purchase and sale power and the target peak shaving and valley filling amount based on the target response amount and the target incentive compensation price, wherein the preset benefit function includes the response amount, the incentive compensation price, the purchase and sale power and the peak shaving and valley filling amount, and the peak shaving and valley filling amount includes the power consumption reduced by the load side during the preset peak power consumption period and the power consumption increased during the preset valley power consumption period;

[0052] The inspection module is used to determine whether the target power purchase and sale amount and the target peak shaving and valley filling amount meet the preset constraints. If not, it returns to minimizing the preset cost function as the optimization goal, and uses the Cplex solver combined with the particle swarm algorithm to solve the target response amount. If it meets the requirements, it determines that the target incentive electricity price, the target power purchase and sale amount and the target peak shaving and valley filling amount are the target strategies for the virtual power plant to participate in market transactions.

[0053] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0054] Processor; and

[0055] Memory for storing programs,

[0056] Wherein, the program includes instructions, which, when executed by the processor, enable the processor to execute any of the above-mentioned methods for virtual power plants to participate in market transactions.

[0057] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute any of the above-mentioned methods for a virtual power plant to participate in market transactions.

[0058] One or more technical solutions provided in the embodiments of the present invention, after building a virtual power plant, solve the target response quantity with minimizing the cost function as the optimization goal, and determine the target incentive compensation price based on the target response quantity and the minimum cost function value, and then use maximizing the preset benefit function as the optimization goal, based on the target response quantity and the target incentive compensation price, solve the target purchase and sale electricity volume and the target peak shaving and valley filling volume, minimize the electricity cost of the virtual plant, maximize the economic benefits of the virtual power plant, and improve the user's electricity experience. Furthermore, by obtaining the target response quantity and the target incentive compensation electricity price with minimizing the electricity cost, the user's enthusiasm for making changes to electricity demand is increased, the power shortage is alleviated, the occurrence of abnormal power consumption caused by the power shortage is avoided, and the power efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0060] Figure 1 A flow chart of a method for a virtual power plant to participate in market transactions provided by an embodiment of the present invention;

[0061] Figure 2 Another flow chart of the method for a virtual power plant to participate in market transactions provided by an embodiment of the present invention

[0062] Figure 3A structural schematic diagram of a device for a virtual power plant to participate in market transactions provided by an embodiment of the present invention;

[0063] Figure 4 A block diagram of an exemplary electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0064] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0065] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0066] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0067] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0068] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0069] The embodiments of the present invention provide a method, device, electronic device and storage medium for a virtual power plant to participate in market transactions. The method for a virtual power plant to participate in market transactions can be applied to a virtual power plant, wherein the virtual power plant is an aggregate of distributed resources, specifically, it can include a power supply side, an energy storage side and a load side, wherein the power supply side includes various power generation equipment, including solar power generation, wind power plants, etc., and this resource is usually distributed in different geographical locations. The energy storage side includes battery energy storage, pumped storage and compressed air energy storage, etc., and the energy storage side can store energy when the power demand is low and release energy when the power demand is high. The load side includes various types of electrical equipment, such as new energy vehicle charging piles, factories, residential electricity, etc.

[0070] As a possible implementation, the above-mentioned load side may include flexible loads, which refer to load devices that can actively change their own load characteristics according to the needs of the power grid and have controllable characteristics. Specifically, the above-mentioned flexible loads may include adjustable loads and transferable loads, wherein the adjustable loads refer to demand-side electrical equipment, power supply equipment and energy storage equipment that can start and stop, adjust the operating status or adjust the operating time period according to electricity prices, incentives or transaction information, such as residents' smart home systems, factory production electricity, etc. Transferable loads refer to loads whose power supply time can be changed according to plan. The electricity consumption of such loads can be flexibly transferred in different time periods, but the total demand for electricity remains unchanged, such as residents' washing machines, dishwashers and other equipment. The following describes the scheme of the present invention with reference to the accompanying drawings:

[0071] like Figure 1 As shown, Figure 1 A flow chart of a method for a virtual power plant to participate in market transactions provided by an embodiment of the present invention may include the following steps:

[0072] S101, obtaining preset parameters of each component of the virtual power plant, where the components include a power supply side, an energy storage side, and a load side;

[0073] S102, taking minimization of a preset cost function as an optimization goal, and using a Cplex solver in combination with a particle swarm algorithm to solve a target response amount, wherein the preset cost function includes a response amount, an incentive compensation price, and an electricity price of the virtual power plant;

[0074] S103, obtaining a target incentive compensation price based on the target response amount and the minimum value of the preset cost function;

[0075] S104, taking maximization of a preset benefit function as an optimization objective, using a Cplex solver, based on the target response amount and the target incentive compensation price, solving the target purchase and sale power and the target peak shaving and valley filling amount, wherein the preset benefit function includes the response amount, the incentive compensation price, the purchase and sale power, and the peak shaving and valley filling amount, and the peak shaving and valley filling amount includes the power consumption reduced by the load side during the preset peak power consumption period and the power consumption increased during the preset valley power consumption period;

[0076] S105, determining whether the target purchase and sale power amount and the target peak shaving and valley filling amount meet the preset constraint conditions, if not, returning to S102, if yes, executing S106;

[0077] S106. Determine the target incentive electricity price, the target electricity purchase and sales amount, and the target peak shaving and valley filling amount as the target strategy for the virtual power plant to participate in market transactions.

[0078] By applying the embodiment of the present invention, after constructing a virtual power plant, the target response quantity is solved by minimizing the cost function as the optimization goal, and the target incentive compensation price is determined based on the target response quantity and the minimum cost function value, and then the target purchase and sale electricity quantity and the target peak shaving and valley filling quantity are solved based on the target response quantity and the target incentive compensation price with maximizing the preset benefit function as the optimization goal, so as to minimize the electricity cost of the virtual plant, maximize the economic benefits of the virtual power plant, and improve the user's electricity experience. Furthermore, by obtaining the target response quantity and the target incentive compensation electricity price by minimizing the electricity cost, the user's enthusiasm for making changes to the electricity demand is increased, the power shortage is alleviated, the occurrence of abnormal power consumption caused by the power shortage is avoided, and the power efficiency is improved.

[0079] As described above, the method for a virtual power plant to participate in market transactions provided by an embodiment of the present invention can be applied to a virtual power plant. In a possible embodiment, the basic framework of the virtual power plant can be first established, that is, the data of each component of the virtual power plant can be accessed. The components of the virtual power plant may include the above-mentioned power supply side, energy storage side, and load side. As a possible implementation method, the parameters of each component can be obtained through the interface provided by each component of the virtual power plant. The above parameters can be defined according to the actual application scenario. For example, the parameters on the power supply side may include the total power generation of each power generation equipment, the power generation cost, and the power generation in each time; the parameters on the energy storage side may include the energy storage cost; the parameters on the load side may include each load identification, each load power demand, power usage, usage time, electricity price, etc. The above load identification may include adjustable loads and transferable loads.

[0080] In a possible embodiment, the specific form of the above-mentioned components can be a system. Specifically, the virtual power plant can include a power management system, an energy storage management system, and a load management system. Each system can have a unified external interface and a database for storing data. Accordingly, as a possible implementation method, the parameters of each component can be obtained through the database of each system.

[0081] After the virtual power plant is built, the above S101-S106 can be used to determine the strategy of the virtual power plant to participate in market transactions. Specifically, it can include determining the incentive compensation price, the amount of electricity purchased and sold, and the peak-shaving and valley-filling amount of the transferable load, etc. The peak-shaving and valley-filling amount refers to the transferable load, which moves the electricity used during its peak period to the valley period. The above-mentioned purchase and sale of electricity can include the electricity purchased by the load side of the virtual power plant and the electricity sold by the energy storage side. In actual applications, in order to alleviate the power pressure during peak periods of electricity consumption, incentive compensation prices are usually set to stimulate users to actively reduce electricity consumption during peak periods of electricity consumption. Appropriate incentive compensation prices can increase the user's response enthusiasm while ensuring stable production of the power plant.

[0082] In S102, minimizing a preset cost function can be used as an optimization goal, and the market response quantity can be solved by combining the Cplex solver with the particle swarm algorithm. The market response quantity refers to the response quantity of the load participating in the demand response transaction, which can include the amount of electricity transferred by the transferable load and the amount of electricity adjusted by the adjustable load, that is, the reduction value of the electricity used by the adjustable load. The Cplex solver is an open source online calculator project for processing complex number operations and advanced data functions. Using the Cplex solver to solve the market response quantity can improve the efficiency of solving the market response quantity.

[0083] The above cost function can be composed of the electricity cost of the transferable load and the electricity cost of the adjustable load. As mentioned above, the transferable load refers to the load whose electricity demand can be flexibly transferred in different time periods. The model for its participation in market transactions is:

[0084]

[0085] in, is the electricity consumption of the transferable load after market response at time t, It is the initial power consumption of the transferable load at the initial moment. The initial moment can be set according to the actual power consumption scenario, such as 0 o'clock, 5 o'clock, etc. The initial power consumption can be obtained based on the user's historical power consumption, such as the average value, median value, etc. of the user's historical power consumption at the initial moment. To transfer the electricity, To transfer out electricity, When it is 1, it means there is load transfer. When it is 1, it indicates that there is load transfer.

[0086] The electricity cost of the transferable load can be obtained by the following function:

[0087]

[0088] is the electricity price of the virtual power plant, It is the electricity consumption after the transferable load participates in the market transaction. is the transferred power of the transferable load, It is the compensation price set by the virtual power plant for the transferable load users in the ε power consumption scenario. Among them, the power consumption scenario can be divided according to the actual business, such as residential household electricity consumption, factory electricity consumption, etc. The above compensation price refers to the economic incentive provided to encourage users to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours, which is a quantity to be determined. σ and υ are unsatisfactory cost coefficients, and their specific values ​​can be pre-set according to the actual application scenario. For example, σ takes a value of 0.7 and υ takes a value of 0.8. When it is 1, it indicates that there is load transfer in the ε power consumption scenario.

[0089] In actual power application scenarios, due to problems such as insufficient power supply, unstable quality, and untimely service, users may suffer losses and inconveniences in production and life. These losses and inconveniences caused by power problems are called dissatisfaction costs. Dissatisfaction costs can include generation interruptions, equipment losses, and energy waste. Dissatisfaction costs are usually defined by the following formula:

[0090]

[0091] in, is the dissatisfaction cost, ΔS t is the response amount of the load participating in market transactions, σ and υ are the above-mentioned dissatisfaction coefficients, and the dissatisfaction coefficient can define the correlation between the dissatisfaction cost and the response amount of the load participating in market transactions.

[0092] In a possible embodiment, the electricity cost of the adjustable load can be obtained by the following function:

[0093]

[0094] in, is the electricity cost of the adjustable load, is the electricity price of the virtual power plant, The power consumption after the adjustable load participates in the market transaction. is the reduction of the adjustable load, which refers to the difference between the maximum power consumption of the adjustable load and the actual power consumption. It is the incentive compensation price set by the virtual power plant for adjustable load users in the ε electricity consumption scenario.

[0095] The above preset cost function is the sum of the costs of the adjustable load and the transferable load. Specifically, the preset cost function may be:

[0096]

[0097] Among them, C load is the flexible load, N ε is the total number of scenarios, which can be determined according to different types of electricity consumption. It is understandable that factories, schools, residents, hospitals and other units usually have different market trading strategies due to their different electricity consumption scales. Therefore, in a possible embodiment, the strategies for virtual power plants to participate in market transactions can be determined for the above units respectively. Correspondingly, the above total number of scenarios can also be set for different electricity consumption scenarios in the above different units, such as electricity consumption scenarios for different flexible loads in the same industrial park, which can include production line 1, production line 2, and so on.

[0098] In the present invention, in S102, minimizing the preset cost function can be used as an optimization goal, and the response amount can be obtained by using Cplex combined with a particle swarm algorithm. The particle swarm algorithm is an optimization algorithm based on swarm intelligence. Specifically, the particle swarm algorithm can include multiple particles, and the multiple particles can update their own speed and position according to their own optimal value and the global optimal value, and finally find the best position.

[0099] In a possible embodiment, the response amount can be obtained by using the particle swarm algorithm through the following steps:

[0100] S21, setting the number of iterations, the number of particles and the convergence error in the Cplex solver, and initializing the incentive compensation price particles;

[0101] S22, using the Cplex solver to update the speed and position of each of the incentive compensation price particles, and find the minimum value of the preset cost function;

[0102] S23. When the minimum value of the preset cost function is obtained, the corresponding target response amount is determined using the Cplex.

[0103] The particle refers to the incentive compensation price, the number of iterations, the number of particles and the convergence error of the particle swarm algorithm can be flexibly set according to the actual application scenario. The above convergence error refers to the error of the evaluation function, which can be the error of the difference between two evaluation function values.

[0104] In a possible embodiment, the compensation price particle may be initialized first, and the initialization includes initializing the speed and position of the compensation price particle. The initialization speed and the initialization position may be randomly set.

[0105] The particle speed and position can be updated using the following formula:

[0106] v i =ωv i-1 +c1r1t(p i-1 -x i-1 )+c2r2t(g i-1 -x i-1 )

[0107] xi=x i-1 +v i

[0108] Among them, ω, c1, r1, c2, r2 are all preset coefficients, r1 and r2 are random numbers between [0,1], t is the time, v i-1 is the velocity of the particle at the last moment, v i is the updated velocity of the particle, x i-1 is the position of the particle at the last moment, xi is the updated position of the particle, and p i-1 is the global optimal position, g i-1 is the optimal position of the particle. The optimal position can be measured by a cost function. Specifically, the position corresponding to the particle with a smaller cost function value is the global optimal position.

[0109] When the preset stop condition is reached, the current global best particle can be determined, and the response amount obtained based on the particle is the target response amount. The global best particle can be the particle corresponding to the minimum cost function value. The above-mentioned preset stop condition can be that the number of iterations reaches the preset number of iterations and the convergence error is less than the preset convergence error. The convergence error can be the difference between the cost function values ​​obtained from two iterations.

[0110] In a possible embodiment, in the process of solving the target response quantity using the particle swarm algorithm, constraints can also be considered. The constraints can include a range of load that can be reduced and a range of load that can be transferred. The range of load that can be reduced and the range of load that can be transferred can both be preset for each power usage scenario.

[0111] Exemplarily, the above-mentioned load reduction range may be:

[0112]

[0113] in, The maximum value of the load that can be reduced can be determined based on the user's historical electricity usage records. For example, the difference between the minimum and maximum values ​​of the user's historical electricity usage records at time t can be used as the maximum value of the load that can be reduced.

[0114] The above transferable loads may range from:

[0115]

[0116] in, and The upper limits of the transferable value and the transferable value can be determined based on the user's historical electricity consumption records. For example, the upper limit of the transferable value can be determined based on the electricity consumption of other electricity consumption scenarios other than the electricity consumption scenario at time t, or the electricity consumption of the electricity consumption scenario. For example, the upper limit of the transferable value can be the difference between the sum of the minimum electricity consumption values ​​of other electricity consumption scenarios at time t and the sum of their current electricity consumption, or the peak electricity consumption of the electricity consumption scenario at time t, or the difference between the maximum operating load and the minimum operating load and the normal electricity load. It means that the sum of electricity consumption in each electricity consumption scenario is the same at time t.

[0117] In a possible embodiment, if a value that does not satisfy the above constraint condition appears during the process of calculating the target response using the particle swarm algorithm, the particle calculation can be continued based on the speed and / or position of the particle corresponding to the data value. Through the above technical solution, the constraint condition is considered during the process of calculating the target response using the particle swarm algorithm. Since the constraint condition is preset according to the actual power consumption scenario, the obtained target response amount is more in line with the actual situation, that is, the accuracy of the response amount calculation is improved.

[0118] After the target response amount is obtained, the incentive compensation price can be calculated according to the response amount. In a possible embodiment, the target incentive compensation price can be determined based on the cost function using the minimum function value of the cost function obtained above and the target response amount.

[0119] In S104, the purchase and sale of electricity and the amount of peak shaving and valley filling can be solved with the maximum economic benefit as the objective function. The amount of peak shaving and valley filling refers to the amount by which the adjustable load can transfer the power demand from the historical peak value of electricity usage to the historical valley value of electricity usage. The above-mentioned historical peak value of electricity usage and the historical low value of electricity usage can be determined based on the user's historical electricity usage record, which can include the electricity usage and the corresponding electricity usage time.

[0120] The above economic benefits refer to the profits obtained by the virtual power plant, which may include the benefits of the energy market, the benefits of the peak load market, and the benefits obtained by the flexible load, etc. Specifically, the above economic benefit function may be:

[0121]

[0122] Among them, P refers to the economic benefit function, ω represents the electricity usage scenario, and N represents the number of electricity usage scenarios. represents the revenue of the virtual power plant in the energy market, represents the revenue of virtual power plants in the peak load market, represents the benefit obtained by flexible load, C ω represents the operating cost of the virtual power plant, η ε It is a preset coefficient, specifically, a weighting coefficient or efficiency factor, which is used to adjust the weighted cost or load component related to the electricity usage scenario, and its specific value can be set according to the actual application scenario.

[0123] Specifically, the revenue of the above virtual power plant in the energy market is:

[0124]

[0125] in, is the electricity price in the energy market, which may be the incentive compensation electricity price obtained above, When it is 1, it means that electricity sales occur in the electricity usage scenario. When it is 1, it means that electricity purchase occurs in the electricity usage scenario. It represents the amount of electricity purchased in the energy market. Indicates the amount of electricity sold in the energy market for the electricity usage scenario.

[0126] The revenue of the above virtual power plant in the peak load market can be obtained through the following formula:

[0127]

[0128] in,

[0129] in, as well as are the peak shaving amount and valley filling amount to be determined, as well as Indicates the compensation price obtained by peak shaving and valley filling. In actual electricity consumption scenarios, the user response is usually increased by raising the electricity price during peak electricity consumption and lowering the electricity price during valley electricity consumption. Both compensation prices can be obtained by combining the peak shaving and valley filling amount obtained through the correlation between the incentive compensation electricity price and the user response amount. For example, the peak shaving amount and valley filling amount can be used as the target user response amount, and the corresponding incentive compensation electricity price can be obtained based on the above management relationship. Indicates the output of diesel engines in the peak load regulation market. as well as They represent the charging and discharging of energy storage in the peak load regulation market. is the peak shaving amount of the adjustable load in the peak shaving market. as well as It refers to the amount of transferable load transferred out during peak electricity consumption period and the amount of load transferred in during trough electricity consumption period in the peak-shaving market.

[0130] The benefits of the above flexible load can be obtained by the following formula:

[0131]

[0132] Among them, t represents each preset time, T represents the preset statistical duration, and the above t and T can be preset according to different power usage scenarios. For example, t can be set every hour, and T can be set to 24 hours. represents the incentive compensation price of flexible load at time t, and They are respectively the electricity consumption of the transferable load and the adjustable load after participating in the market demand response at time t.

[0133] The operating cost of the above virtual power plant can be obtained by the following formula:

[0134]

[0135] in, The cost of energy storage operation. The cost of diesel unit operation. The cost of providing incentive compensation for virtual power plants.

[0136] In a possible embodiment, the relevant parameters in the above formula can be input into the Cplex solver, and the target power purchase and sales amount and the peak shaving and valley filling amount can be solved by the particle swarm algorithm.

[0137] Specifically, based on the above-mentioned compensation electricity price and response amount, Cplex can be used to take the maximum value of the economic benefit function as the optimization goal to obtain the optimal purchase and sale of electricity and the peak-shaving and valley-filling amount.

[0138] In a possible embodiment, when obtaining the optimal amount of electricity purchased and sold and the amount of peak load shifting and valley filling, constraints can also be set according to the electricity usage scenario. Exemplarily, the above constraints may include: market transaction condition constraints, energy storage constraints, diesel unit constraints, and participant balance constraints. The above constraints are exemplarily described below:

[0139] The above market transaction condition constraints can be:

[0140]

[0141] in, represents the maximum amount of electricity sold in the energy market, is the maximum amount of electricity purchased in the energy market, and the two maximum values ​​can be determined based on the power plant's electricity consumption and the user's electricity consumption history. For example, the maximum amount of electricity sold can be the maximum capacity of the power plant at time t, or the maximum energy storage, and the maximum amount of electricity purchased can be the sum of the maximum values ​​of electricity purchased by each user in the user's history records.

[0142] The above energy storage constraints can be:

[0143]

[0144] in, as well as They represent the charging and discharging amount of energy storage at time t respectively. When it is 1, it means the energy storage is in charging state. When it is 1, it means the energy storage is in the discharge state. as well as They represent the maximum value of the energy storage charging power and the maximum value of the discharging power respectively.

[0145] The above diesel engine group constraints can be:

[0146]

[0147] in, as well as They are the minimum output and maximum output of the diesel generator set respectively. The minimum output and maximum output can be determined according to the actual performance of the diesel generator set. Specifically, they can be determined according to the minimum power and maximum power of the diesel generator set.

[0148] The above-mentioned balance conditions for the parties can be:

[0149]

[0150] in, is the output of the diesel generator set at time t, To discharge the energy storage, Charging the energy storage, For adjustable load power consumption, For transferable load electricity, is the electricity sales of the virtual power plant, The amount of electricity purchased by the virtual power plant.

[0151] In a possible embodiment, after obtaining the above-mentioned power purchase and sales amount and peak shaving and valley filling amount, it can be determined whether the power purchase and sales amount and peak shaving and valley filling amount meet the above-mentioned constraints. If the above-mentioned constraints are met, the current power purchase and sales amount and peak shaving and valley filling amount are determined to be the target power purchase and sales amount and target peak shaving and valley filling amount.

[0152] In a possible embodiment, after obtaining the parameters of each component of the virtual power plant, it can also be determined whether the parameters are abnormal based on the above constraints.

[0153] In a possible embodiment, after obtaining a round of candidate response quantities, incentive compensation prices, and electricity purchase and sales, and peak shaving and valley filling quantities, it is determined whether a preset stop condition is reached. The preset stop condition may be that the number of iterations is greater than a preset number, and the convergence error is less than a preset convergence error. If not, the number of iterations is increased and the particles are updated, including updating the speed and position of the particles. If so, it is determined that the target electricity purchase and sales and peak shaving and valley filling quantities are obtained.

[0154] In a possible embodiment, a strategy set for a virtual power plant and a strategy set for flexible loads may be established, and Cplex may be used to find the optimal solution between the two strategy sets. For example, the following strategy sets may be constructed:

[0155]

[0156] Among them, y1 and y2 are the game strategy sets of virtual power plants; z is the flexible load game strategy set. Based on the above strategy sets, the optimal solution can be solved according to the following optimization objectives:

[0157]

[0158] like Figure 2 As shown, Figure 2 A specific flow chart of a method for a virtual power plant to participate in market transactions provided by an embodiment of the present invention may include the following steps:

[0159] S201. Establish the basic framework of source, load and storage.

[0160] S202: Initialize the basic parameters of source, load and storage, and establish a master-slave decision model.

[0161] Specifically, the above parameters can be input into the Cplex solver.

[0162] S203, setting parameters such as the number of iterations, the number of particles, and the convergence error.

[0163] S204, initializing incentive compensation price particles.

[0164] S205. Enter the first iteration.

[0165] S206. Initialize the flexible load power consumption.

[0166] S207, taking the lowest electricity cost as the objective function and considering the constraints, use the Cplex solver to find the optimal solution response.

[0167] S208. Calculate the incentive compensation price according to the load response amount.

[0168] S209, taking the optimal economic benefit as the objective function and combining with the preset constraints, the Cplex solver is used to find the optimal amount of electricity purchased and sold and the amount of peak shaving and valley filling.

[0169] S210, determine whether r≥R and △ω≤σ, if not, update the particle and increase the number of iterations by 1, return to S206, if yes, determine to obtain the optimal purchase and sale of electricity and peak shaving and valley filling, where R is the maximum number of iterations, and σ is the preset convergence error.

[0170] As the master-slave game interaction between the optimal profit in the electric energy market and the lowest electricity cost for flexible loads in the peak-shaving market continues to deepen, the game strategies of the two show a stable trend. After reaching a certain number of times and meeting the convergence error requirements, the optimal market trading strategy will be achieved.

[0171] By applying the embodiment of the present invention, with the goal of minimizing electricity costs and taking various constraints into consideration, the solver Cplex is used to solve the optimal response power of the flexible load of the virtual power plant in the trading market. Then, with the goal of optimizing economic benefits and taking various constraints into consideration, the solver Cplex is used to solve the optimal purchase and sale power of the virtual power plant participating in market transactions and the peak-shaving and valley-filling power. By establishing a master-slave game strategy for both the lowest electricity cost and the best economic benefits, the optimal strategy for the virtual power plant to participate in market transactions is formulated, bringing the greatest economic benefits to customers.

[0172] Based on the same inventive concept, an embodiment of the present invention further provides a device for a virtual power plant to participate in market transactions, the device being applied to a virtual power plant, the virtual power plant comprising a power supply side, an energy storage side and a load side, the load side comprising an adjustable load and a transferable load, such as Figure 3 As shown, the device 300 may include:

[0173] An acquisition module 301 is used to acquire preset parameters of various components of the virtual power plant, including a power supply side, an energy storage side, and a load side;

[0174] The first solving module 302 is used to solve the target response quantity by minimizing the preset cost function as the optimization goal, using the Cplex solver combined with the particle swarm algorithm, wherein the preset cost function includes the response quantity, the incentive compensation price and the electricity price of the virtual power plant;

[0175] A calculation module 303 is used to obtain a target incentive compensation price based on the target response amount and the minimum value of the preset cost function;

[0176] The second solving module 304 is used to maximize the preset benefit function as the optimization goal, and use the Cplex solver to solve the target purchase and sale power and the target peak-shaving and valley-filling amount based on the target response amount and the target incentive compensation price, wherein the preset benefit function includes the response amount, the incentive compensation price, the purchase and sale power and the peak-shaving and valley-filling amount, and the peak-shaving and valley-filling amount includes the power consumption reduced by the load side during the preset peak power consumption period and the power consumption increased during the preset valley power consumption period;

[0177] The inspection module 305 is used to determine whether the target power purchase and sale amount and the target peak shaving and valley filling amount meet the preset constraints. If not, it returns to minimizing the preset cost function as the optimization goal, and uses the Cplex solver combined with the particle swarm algorithm to solve the target response amount. If it meets the requirements, it determines that the target incentive electricity price, the target power purchase and sale amount and the target peak shaving and valley filling amount are the target strategies for the virtual power plant to participate in market transactions.

[0178] Among them, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the present invention are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0179] The exemplary embodiment of the present invention further provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform a method according to an embodiment of the present invention when executed by the at least one processor.

[0180] Exemplary embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0181] An exemplary embodiment of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform a method according to an embodiment of the present invention.

[0182] refer to Figure 4, a block diagram of an electronic device 400 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0183] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0184] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 may be any type of device capable of inputting information to the electronic device 400, and the input unit 406 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 407 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 may include, but is not limited to, a disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0185] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the method for the above-mentioned virtual power plant to participate in market transactions may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. In some embodiments, the computing unit 401 may be configured to execute the above-mentioned method for the virtual power plant to participate in market transactions by any other appropriate means (e.g., by means of firmware).

[0186] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0187] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0189] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0190] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0191] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

Claims

1. A method for a virtual power plant to participate in market transactions, characterized in that: The method is applied to a virtual power plant, which includes a power supply side, an energy storage side, and a load side, wherein the load side includes an adjustable load and a transferable load, and the method includes: Obtaining preset parameters of each component of the virtual power plant, wherein the components include a power supply side, an energy storage side, and a load side; Taking minimization of a preset cost function as the optimization goal, the target response quantity is solved by using a Cplex solver combined with a particle swarm algorithm, wherein the preset cost function includes the response quantity, the incentive compensation price, and the electricity price of the virtual power plant; Based on the target response amount and the minimum value of the preset cost function, obtaining a target incentive compensation price; Taking maximizing the preset benefit function as the optimization goal, using the Cplex solver, based on the target response amount and the target incentive compensation price, the target purchase and sale power amount and the target peak shaving and valley filling amount are solved, wherein the preset benefit function includes the response amount, the incentive compensation price, the purchase and sale power amount and the peak shaving and valley filling amount, and the peak shaving and valley filling amount includes the power consumption reduced by the load side during the preset peak power consumption period and the power consumption increased during the preset valley power consumption period; Determine whether the target power purchase and sale amount and the target peak shaving and valley filling amount meet the preset constraints. If not, return to the step of minimizing the preset cost function as the optimization goal, and use the Cplex solver combined with the particle swarm algorithm to solve the target response amount. If they meet the requirements, determine the target incentive electricity price, the target power purchase and sale amount, and the target peak shaving and valley filling amount as the target strategy for the virtual power plant to participate in market transactions.

2. The method according to claim 1, characterized in that The virtual power plant further includes a power management system, an energy storage management system, and a load management system. The obtaining of preset parameters of each component of the virtual power plant includes: Access the power management system, energy storage management system and load management system, and obtain preset parameters of each component of the virtual power plant according to a preset data acquisition method, wherein the preset data acquisition method includes: obtaining through an interface and obtaining through a database.

3. The method according to claim 1, characterized in that The preset cost function includes a cost function of an adjustable load and a cost function of a transferable load, wherein the cost function of the adjustable load is: in, is the electricity cost of the adjustable load, is the electricity price of the virtual power plant, is the response of the adjustable load, σ and υ are the unsatisfactory cost coefficients, is the reduction amount of the adjustable load, is the incentive compensation price set by the virtual power plant for the adjustable load user in the ε electricity consumption scenario; The cost function of the transferable load is: in, is the electricity cost of the transferable load, is the response amount of the transferable load, is the transfer-out amount of the transferable load, is the incentive compensation price set by the virtual power plant for the transferable load user in the ε electricity consumption scenario, When it is 1, it indicates that there is load transfer in the ε power consumption scenario; The electricity cost function is: Among them, C load is the electricity cost on the load side, N ε is the total number of electricity consumption scenarios, η ε is the preset coefficient.

4. The method according to claim 3, characterized in that The minimization of the preset cost function is taken as the optimization goal, and the target response quantity is solved by using the Cplex solver combined with the particle swarm algorithm, including: Setting the number of iterations, the number of particles, and the convergence error in the Cplex solver, and initializing the incentive compensation price particles; Using the Cplex solver to update the speed and position of each of the incentive compensation price particles, and find the minimum value of the preset cost function; When the minimum value of the preset cost function is obtained, the corresponding target response amount is determined using the Cplex.

5. The method according to claim 4, characterized in that The method further comprises: Determine whether the incentive compensation price particle and the target response amount meet the preset first constraint condition. If not, return to the step of using the Cplex solver to update the speed and position of each incentive compensation price particle and find the minimum value of the preset cost function, wherein the first constraint condition includes: a reduction condition and a transfer condition, wherein the reduction condition is: in, is a preset maximum value of the load that can be curtailed, wherein the maximum value of the load that can be curtailed is obtained based on the maximum power consumption of the adjustable load participating in the demand response; The transfer conditions are: in, and is the upper limit of the transferable in and out values ​​of the transferable load at time t, It means that the sum of electricity consumption in each electricity consumption scenario is the same at time t.

6. The method according to claim 1, characterized in that The preset benefit function is: Among them, P is the economic benefit, ω represents the electricity usage scenario, and N represents the number of electricity usage scenarios. represents the revenue of the virtual power plant in the energy market, represents the revenue of the virtual power plant in the peak load market, represents the revenue obtained by the load side, C ω Represents the operating cost of the virtual power plant, wherein the income of the virtual power plant in the energy market is positively correlated with the incentive compensation electricity price and the electricity sales amount of the virtual power plant, and negatively correlated with the electricity purchase amount; the income of the virtual power plant during the peak-shaving period is positively correlated with the peak-shaving and valley-filling amount, the incentive compensation electricity price during the peak electricity consumption period, and the incentive compensation electricity price during the valley electricity consumption period; the income obtained by the load side is positively correlated with the response amount and the incentive compensation price.

7. The method according to claim 1, characterized in that The preset constraints include: market transaction condition constraints, energy storage constraints, diesel unit constraints and participant balance constraints, wherein the market transaction condition constraints are: in, is the electricity sales volume of the virtual power plant under the electricity consumption scenario ε at time t, is the amount of electricity purchased by the virtual power plant under the electricity consumption scenario ε at time t, is the maximum amount of electricity that can be sold in the virtual power plant at time t, is the maximum amount of electricity that can be purchased in the virtual power plant at time t; The energy storage constraint is: in, as well as They respectively represent the charge and discharge amounts of the energy storage side at time t, When it is 1, it indicates that the energy storage side is in charging state. When it is 1, it indicates that the energy storage side is in a discharging state. as well as Respectively represent the maximum value of charging power and the maximum value of discharging power on the energy storage side; The diesel group constraints are: in, as well as are the minimum and maximum output of the diesel generator set, is the output of the diesel generator set at time t; in, is the output of the diesel generator set at time t, is the energy storage side discharge at time t, To charge the energy storage side at time t, is the adjustable load power consumption at time t, is the transferable load power consumption at time t, is the electricity sales of the virtual power plant at time t, is the amount of electricity purchased by the virtual power plant at time t.

8. A device for a virtual power plant to participate in market transactions, characterized in that: The device is applied to a virtual power plant, which includes a power supply side, an energy storage side, and a load side, wherein the load side includes an adjustable load and a transferable load, and the device includes: An acquisition module, used to acquire preset parameters of various components of the virtual power plant, wherein the components include a power supply side, an energy storage side, and a load side; A first solving module is used to solve the target response quantity by minimizing a preset cost function as an optimization goal and using a Cplex solver combined with a particle swarm algorithm, wherein the preset cost function includes the response quantity, the incentive compensation price, and the electricity price of the virtual power plant; A calculation module, used to obtain a target incentive compensation price based on the target response amount and a minimum value of the preset cost function; The second solving module is used to maximize the preset benefit function as the optimization goal, and use the Cplex solver to solve the target purchase and sale power and the target peak shaving and valley filling amount based on the target response amount and the target incentive compensation price, wherein the preset benefit function includes the response amount, the incentive compensation price, the purchase and sale power and the peak shaving and valley filling amount, and the peak shaving and valley filling amount includes the power consumption reduced by the load side during the preset peak power consumption period and the power consumption increased during the preset valley power consumption period; The inspection module is used to determine whether the target power purchase and sale amount and the target peak shaving and valley filling amount meet the preset constraints. If not, it returns to minimizing the preset cost function as the optimization goal, and uses the Cplex solver combined with the particle swarm algorithm to solve the target response amount. If it meets the requirements, it determines that the target incentive electricity price, the target power purchase and sale amount and the target peak shaving and valley filling amount are the target strategies for the virtual power plant to participate in market transactions.

9. An electronic device, comprising: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-7.

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