Virtual power plant scheduling method and device and virtual power plant scheduling system

Abstract

The invention provides a virtual power plant scheduling method and device and a virtual power plant scheduling system, and the method comprises the steps: building a maximum profit function which is used for calculating the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balance energy market on the day; establishing constraint conditions for the parameters of the profit maximization function, wherein the parameters of the profit maximization function comprise sales power on the day-ahead market, purchase power on the day-ahead market, upward reserve power on the balance energy market and downward reserve power on the balance energy market; under the condition that constraint conditions are met, a maximum value is obtained through calculation by adopting a profit maximum function, and the maximum profit of the day is obtained; and performing power dispatching on the virtual power plant according to the parameter of the profit maximization function corresponding to the maximum profit of the current day. The problem that profit maximization is difficult to realize by a dispatching method of the virtual power plant in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a dispatching method, device, computer-readable storage medium, computer program product and virtual power plant dispatching system for a virtual power plant. Background Art

[0002] With the rapid development of renewable energy sources (RES), virtual power plants (VPPs) participate in electricity market transactions as unified agents that integrate distributed generation resources, energy storage systems (ESS) and loads. Virtual power plants can improve the flexibility and economy of power supply by optimizing internal resource coordination. However, since energy storage systems not only change the charging and discharging power when activating reserve supply capacity, but also involve changes in energy levels, this brings new challenges to the scheduling strategy of virtual power plants.

[0003] When considering the reserve supply provided by energy storage systems in the prior art, attention is usually only paid to the upward reserve supply in the contract signed with the balancing energy market, without considering the upward reserve or downward reserve services that can be provided outside the contract. Summary of the invention

[0004] The main purpose of this application is to provide a virtual power plant scheduling method, device, computer-readable storage medium, computer program product and virtual power plant scheduling system, so as to at least solve the problem that the virtual power plant scheduling method in the prior art is difficult to maximize profits.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a scheduling method for a virtual power plant is provided, including: establishing a profit maximization function, wherein the profit maximization function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit in the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand; establishing constraints on the parameters of the profit maximization function, the parameters of the profit maximization function include the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market; when the constraints are met, the profit maximization function is used to calculate the maximum value to obtain the maximum profit for the day; and the virtual power plant is dispatched for electricity according to the parameters of the profit maximization function corresponding to the maximum profit for the day.

[0006] Optionally, establish a profit maximization function, including: As the profit maximization function, A collection of times of the day is the set of scenarios with uncertain parameters, is the day-ahead market price at time t, is the price of upward energy storage at time t, is the price of storing energy downward at time t, Δt is the period of power adjustment, π s is the probability that the scenario at time t is scenario s, p act Balance the probability of energy reserve activation, The power sold at time t in the day-ahead market, The purchased power at time t in the day-ahead market is, At time t in the scenario s, the virtual power plant reserves upward power in the balancing energy market, At time t in the scenario s, the virtual power plant reserves power downward in the balancing energy market.

[0007] Optionally, the virtual power plant includes an energy storage system. Before establishing constraints on the parameters of the profit maximization function, the method further includes: establishing state constraints on the energy storage system. and in, is a binary state variable that provides the state of the upward reserve at time t under scenario s, is a binary state variable that provides the state of the downward reserve at time t under scenario s, is a binary state variable of the charge and discharge state of the energy storage system at time t under scenario s, is a binary state variable of the charge and discharge state of the energy storage system after activating the reserve energy at time t under scenario s; establishes the reserve energy form constraint condition and in, Indicates that the energy storage system provides downward reserve power in the discharge state at time t of scenario s, In the scenario s at time t, the energy storage system provides downward reserve power in the charging state, Indicates that the energy storage system provides upward reserve power in the discharge state at time t of scenario s, Indicates that the energy storage system provides upward reserve power in the charging state at time t of scenario s, and M is a constant; establish the upward reserve constraint condition of the energy storage system

[0008] and Among them, P ESSis the rated power of the energy storage system; establish the downward reserve constraint condition of the energy storage system

[0009] and

[0010] Optionally, the virtual power plant also includes a renewable energy system, and constraints are established on the parameters of the profit maximization function, including: establishing constraints on purchasing and selling electricity in the day-ahead market to obtain multiple first constraints, the first constraints include sales power constraints and purchase power constraints; establishing constraints on upward reserves and downward reserves in the balancing energy market to obtain multiple second constraints, the second constraints include active power balance constraints, operating constraints of the renewable energy system, operating constraints of the energy storage system, and constraints on reserve energy supply.

[0011] Optionally, establishing constraints on power purchase and power sales in the day-ahead market to obtain a plurality of first constraints includes: establishing the power sales power constraint And the power purchase constraints in, is the power sold to the grid by the renewable energy system at time t in scenario s, is the power sold by the energy storage system to the grid at time t in scenario s, P RES is the rated power of the renewable energy system, is the power purchased by the load from the grid at time t in scenario s, is the power purchased by the energy storage system from the grid at time t in scenario s, P load_max is the maximum possible value of power demand.

[0012] Optionally, based on the constraint conditions of upward reserve and downward reserve in the balancing energy market, a plurality of second constraint conditions are obtained, including: and As the active power balance constraint condition, is the predicted power demand at time t in scenario s, is the output power of renewable energy to meet the load demand at time t in scenario s, is the output power of the energy storage system at time t in scenario s to meet the load demand; and As the operating constraints of the renewable energy system, is the actual output power of the renewable energy system at time t in scenario s, P RESminis the minimum level of output power of the renewable energy system, is the predicted output power of the renewable energy system at time t in scenario s, is the upward reserve power of the renewable energy system at time t in scenario s, represents the downward reserve power of renewable energy at time t in scenario s, is the power used to charge the energy storage system from the renewable energy system at time t in scenario s;

[0013] and As the operating constraints of the energy storage system, is the power of the energy storage system at the beginning of the day in scenario s, is the power of the energy storage system at the end of the day in scenario s; and As the constraint condition of the reserve energy supply, RE t is the upward reserve capacity in the balancing energy market contract at time t, It is a binary state variable indicating whether the virtual power plant provides upward reserve in the balancing energy market contract at time t.

[0014] According to another aspect of the present application, a scheduling device for a virtual power plant is provided, comprising: a first establishing unit, used to establish a profit maximization function, wherein the profit maximization function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit in the balancing energy market on the same day, wherein the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand; a second establishing unit, used to establish constraints on the parameters of the profit maximization function, wherein the parameters of the profit maximization function include the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market; a calculating unit, used to calculate the maximum value using the profit maximization function to obtain the maximum profit for the day when the constraints are met; and a scheduling unit, used to schedule electricity for the virtual power plant according to the parameters of the profit maximization function corresponding to the maximum profit for the day.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0016] According to another aspect of the present application, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, any one of the methods described above is implemented.

[0017] According to another aspect of the present application, a virtual power plant scheduling system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0018] By applying the technical solution of the present application, in the above-mentioned virtual power plant scheduling method, due to the instability of renewable energy and electricity demand in the virtual power plant, there may be a shortage or surplus in the plan for the same-day purchase and sale of electricity. This method not only formulates a plan for the same-day purchase and sale of electricity through the day-ahead market, but also purchases and sells electricity on the same day in the balancing energy market according to the real-time changes in electricity supply and demand to make up for the shortage or surplus, thereby calculating the sum of the profit of the virtual power plant in the day-ahead market and the profit in the balancing energy market, establishing a profit maximization function, and calculating the maximum profit for the day while satisfying the constraints corresponding to the parameters of the profit maximization function. The virtual power plant is dispatched for electricity according to the parameters of the profit maximization function corresponding to the maximum profit for the day, thereby achieving profit maximization, which solves the problem that the virtual power plant scheduling method in the prior art is difficult to achieve profit maximization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A hardware structure block diagram of a mobile terminal for executing a scheduling method for a virtual power plant provided in an embodiment of the present application is shown;

[0020] Figure 2 A schematic diagram of a flow chart of a virtual power plant scheduling method provided according to an embodiment of the present application is shown;

[0021] Figure 3 A schematic diagram of a two-stage scheduling strategy according to an embodiment of the present application is shown;

[0022] Figure 4 A structural block diagram of a scheduling device for a virtual power plant provided according to an embodiment of the present application is shown.

[0023] The above drawings include the following reference numerals:

[0024] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0029] Upward reserve: The short-term estimated power supply capacity of the virtual power plant exceeds the power planned by the day-ahead market, and it needs to sell power to the balancing energy market in the short term;

[0030] Upward reserve: The virtual power plant's short-term estimated power supply capacity is lower than the power planned by the day-ahead market, and it is necessary to purchase power from the balancing energy market in the short term.

[0031] As introduced in the background technology, the scheduling method of virtual power plants in the prior art is difficult to maximize profits. To solve this technical problem, the embodiments of the present application provide a scheduling method, device, computer-readable storage medium, computer program product and virtual power plant scheduling system for a virtual power plant.

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1FIG. 1 is a hardware structure block diagram of a mobile terminal of a virtual power plant scheduling method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0034] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the Z method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] In this embodiment, a scheduling method for a virtual power plant running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 2is a flow chart of a scheduling method for a virtual power plant according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0037] Step S201, establishing a maximum profit function, the maximum profit function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand;

[0038] Step S202, establishing constraint conditions for the parameters of the profit maximum function, the parameters of the profit maximum function including the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market;

[0039] Step S203, when the above constraints are met, the above profit maximum function is used to calculate the maximum value to obtain the maximum profit for the day;

[0040] Step S204, dispatching power from the virtual power plant according to the parameters of the maximum profit function corresponding to the maximum profit of the day.

[0041] In the above-mentioned virtual power plant dispatching method, due to the instability of renewable energy and electricity demand in the virtual power plant, there may be a shortage or surplus in the plan for daily purchase and sale of electricity. This method not only formulates a plan for daily purchase and sale of electricity through the day-ahead market, but also purchases and sells electricity on the same day in the balancing energy market according to the real-time changes in electricity supply and demand to make up for the shortage or surplus, thereby calculating the sum of the profit of the virtual power plant in the day-ahead market and the profit in the balancing energy market, establishing a profit maximization function, and calculating the maximum profit for the day while satisfying the constraints corresponding to the parameters of the profit maximization function. The virtual power plant is dispatched according to the parameters of the profit maximization function corresponding to the maximum profit for the day, thereby achieving profit maximization, which solves the problem that the virtual power plant dispatching method in the prior art is difficult to achieve profit maximization.

[0042] In order to maximize profits, in an optional implementation, the above step S201 includes:

[0043] Step S2011: As the above profit maximization function, A collection of times of the day is the set of scenarios with uncertain parameters, is the day-ahead market price at time t, is the price of upward energy storage at time t, is the price of storing energy downward at time t, Δt is the period of power adjustment, π s is the probability that the scenario at time t is scenario s, p act Balance the probability of energy reserve activation, The sales power at time t in the above day-ahead market is, The purchase power at time t in the above day-ahead market is, At time t in the scenario s, the upward reserve power of the virtual power plant in the balancing energy market is: At time t in the above scenario s, the above virtual power plant reserves downward power in the above balancing energy market.

[0044] In the above implementation, the two-stage scheduling strategy, such as Figure 3As shown in the figure, the first stage is to determine the trading strategy of the day-ahead market, and the second stage is to consider the uncertainty of renewable energy and demand and adjust the unbalanced energy market. Large virtual power plants that integrate multiple renewable energy, energy storage systems and flexible loads are capable of participating in the balancing market, especially when the energy storage system in the virtual power plant accounts for a high proportion of the power structure. The energy storage system has the ability to quickly adjust power, which allows the virtual power plant to supply upward and downward reserves more flexibly. In the first stage, the virtual power plant makes hourly forecasts of the available output power and demand of distributed energy, formulates the corresponding supply and demand plan on the operating day and uploads it to the day-ahead market. The uploaded supply and demand plan cannot be changed in actual production, otherwise it will be punished by the penalty system. The day-ahead market makes reasonable plans for the obtained supply and demand plans and formulates corresponding trading strategies. However, inaccurate forecasts of renewable energy and changes in the number of load users may cause the plan formulated by the day-ahead market to be mismatched with the actual production, resulting in surplus or shortage. At this time, the power system operator needs to balance the market. In the second stage, the virtual power plant receives the latest short-term forecast data of renewable energy and demand. The extremely short-term forecast results are quite accurate, so the virtual power plant can use them as actual data. The virtual power plant determines its trading strategy of upward reserve or downward reserve in the balancing energy market every fifteen minutes. The energy storage system can flexibly increase or decrease the charge and discharge level to achieve mutual conversion between the charging state and the discharging state. It can not only provide upward reserve, but also provide downward reserve, and can obtain more profits while balancing the energy market. It should be noted that the energy storage system is not like other resources that can always have the ability to continuously supply or absorb energy, because when using the energy storage supply capacity, it will not only increase or decrease the charge and discharge power, but also change the energy level stored by the system itself. The two-stage scheduling strategy is to determine the power sales and purchase strategies of the virtual power plant in the day-ahead market when the information about the available power output of renewable energy and customer power consumption is only forecast data. On the basis of the virtual power plant providing contract reserve energy for the balancing energy market, considering the probability of the virtual power plant being selected to provide reserve energy, the backup energy supply capacity of the virtual power plant in the balancing energy market is estimated. By coordinating the operation plan and charging and discharging status of the energy storage system, the total revenue of the virtual power plant in the day-ahead market and the balancing energy market is maximized. In the day-ahead market, the trading strategy of the virtual power plant is a first-stage variable that is determined every hour within 24 hours, and remains unchanged even if the actual data of renewable energy and demand are different from the forecast results. In contrast, the upward or downward reserve energy of the virtual power plant in the balancing energy market, as well as the operating parameters of renewable energy and energy storage systems can be regarded as second-stage variables. Since the time unit of the balancing energy market is usually 15 minutes, the variables in the second stage are determined in quarters of an hour.It is assumed that the day-ahead forecast errors of renewable energy and demand follow a normal distribution with a mean of zero. Therefore, each scenario can be represented by the sum of the forecast value and the random value generated by the forecast error distribution function. The objective function is to maximize the profit of the virtual power plant in the day-ahead market and the balancing energy market within one day. It mainly consists of two parts. The first part is the profit obtained from the day-ahead market, and the second part is the expected profit of selling reserve services in the balancing energy market. Each time period is 15 minutes, with a total of 96 time periods. The profit maximization function can be expressed as.

[0045] In order to consider the state and power limitation of the energy storage system, in an optional implementation manner, the virtual power plant includes an energy storage system, and before establishing constraint conditions on the parameters of the profit maximum function, the method further includes:

[0046] Step S301: Establishing state constraints of the energy storage system and in, is a binary state variable that provides the state of the upward reserve at time t under scenario s, is a binary state variable that provides the state of the downward reserve at time t under scenario s, is the binary state variable of the charging and discharging state of the above energy storage system at time t under scenario s, is a binary state variable of the charge and discharge state of the energy storage system after the energy storage system activates the stored energy at time t under scenario s;

[0047] Step S302: Establishing energy reserve form constraints and in, Indicates that the above energy storage system provides downward reserve power in the discharge state at time t of scenario s, In the scenario s at time t, the above energy storage system provides downward reserve power in the charging state, Indicates that the above energy storage system provides upward reserve power in the discharge state at time t of scenario s, It represents the upward reserve power provided by the above energy storage system in the charging state at time t of scenario s, and M is a constant;

[0048] Step S303: Establishing upward reserve constraints of the energy storage system

[0049] and Among them, P ESS is the rated power of the above energy storage system;

[0050] Step S304: Establishing downward reserve constraints for the energy storage system

[0051] and

[0052] In the above implementation, the energy storage system plays a vital role in the operation of the energy and reserve market of the virtual power plant. It can compensate for the output power of renewable energy and the consumption fluctuations of users, obtain profits through energy exchange in the energy market, and provide reserve services in the balancing market. However, the energy storage system needs to be reasonably planned so that it can provide better services. The upward reserve and downward reserve that the energy storage system can provide need to be jointly determined based on its own energy level and the day-ahead operation plan. Upward reserve and downward reserve cannot be achieved at the same time. Formula (1) gives the constraint condition in, represents the state of providing upward reserve at time t under scenario s, to provide upward reserve, otherwise 0; Indicates the state of providing downward reserve, To provide downward reserve, otherwise it is 0. The energy storage system usually participates in the energy market in the form of charging and discharging. When providing reserve energy, the energy storage system may be in a charging state or a discharging state, which causes the energy storage system to have multiple state changes. represents the charging and discharging state of the energy storage system at time t under scenario s, Indicates the discharge state of the energy storage system, otherwise it is 0; represents the charging and discharging state of the energy storage system after activating the stored energy at time t in scenario s, Indicates the discharge state of the energy storage system after the reserve energy is activated, otherwise it is 0. According to the reserve energy type and the state of the energy storage system before and after the reserve is activated, there are four feasible situations. Formula (2) limits other unachievable states. For example, the energy storage system is normally in a discharge state, and it is required to provide upward reserve. When the energy storage system is in a charging state after the reserve energy is activated, this situation cannot be achieved. Formula (2) is specifically expressed as: In addition, the energy storage system can only provide one form of reserve at the same time, and cannot provide both upward and downward reserves. Formulas (3) and (4) impose relevant constraints on the form of reserve energy provided by the energy storage system, and the big M method is used in the constraints: in, Indicates that the energy storage system provides downward reserve power in the discharge state at time t of scenario s, Indicates that the energy storage system provides downward reserve power in the charging state at time t of scenario s, Indicates that the energy storage system provides upward reserve power in the discharge state at time t of scenario s, represents the upward reserve power provided by the energy storage system in the charging state at time t of scenario s, and M represents a sufficiently large constant. The upward reserve provided by the energy storage system needs to be constrained by actual conditions. Formulas (5)-(8) give the relevant constraints:

[0053] Among them, P ESS Indicates the rated power of the energy storage system. When the energy storage system charging power is 0, the constraint of formula (6) is Formula (7) is not binding, while formula (8) is binding No more than when When, the constraint of formula (6) Formula (8) constraint by restrictions; when When , equations (6) and (8) become unconstrained, and equation (5) becomes Limit to Formula (7) constraint The downward reserve provided by the energy storage system needs to be constrained by actual conditions. Formulas (9)-(12) give the relevant constraints:

[0054]

[0055] when When , equations (10) and (12) become unconstrained, and equation (9) becomes Limit to Formula (11) constraint when When, the constraint of formula (10) Formula (12) constraint by restrictions; when When the energy storage system discharge power is 0, the constraint of formula (10) is Formula (11) is not binding, while formula (12) is binding No more than Assume that under the predetermined operation plan in the energy market, the energy level of the energy storage system after time interval t is After providing upward reserve or downward reserve, the change in energy level of the energy storage system is Specifically expressed as: Among them, η is the charging and discharging efficiency of the energy storage system, and Δt is the duration of the time interval. It can be seen that when the storage is downward Positive value, upward reserve is a negative value. Formula (14) ensures that the reserve energy supply of the energy storage system does not affect its dispatch in the energy market. Formula (15) constrains that the energy level of the energy storage system at any time should be limited by its rated capacity.

[0056] in, represents the energy change of the energy storage system after providing upward reserve or downward reserve at time t = 0 in scenario s, It represents the energy change of the energy storage system after providing upward reserve or downward reserve at t=96 in scenario s, represents the energy of the day-ahead market energy storage system at time t in scenario s, E ESS Indicates the rated capacity of the energy storage system.

[0057] In order to fully consider the constraints of the actual situation, in an optional implementation, the virtual power plant further includes a renewable energy system, and the step S202 includes:

[0058] Step S2021, establishing the constraints of power purchase and power sale in the day-ahead market to obtain a plurality of first constraints, wherein the first constraints include power sale power constraints and power purchase power constraints;

[0059] Step S2022, establishes the upward reserve and downward reserve constraints of the above-mentioned balancing energy market to obtain multiple second constraints, and the above-mentioned second constraints include active power balance constraints, operation constraints of the above-mentioned renewable energy system, operation constraints of the above-mentioned energy storage system and reserve energy supply constraints.

[0060] In the above implementation, the first-stage restriction constraints and the second-stage restriction constraints, i.e., the first constraint conditions and the second constraint conditions, are established. The trading strategy of the virtual power plant in the day-ahead market is completed under the first-stage constraints, and the trading strategy of the virtual power plant in the balancing energy market is completed under the second-stage constraints, so as to be close to the actual scenario and reduce prediction errors.

[0061] In order to consider the restrictions and constraints of the first stage, in an optional implementation, the above step S2021 includes:

[0062] Step S20211: Establish the above power selling power constraint conditions And the power purchase constraints in, is the power sold to the grid by the above renewable energy system at time t in scenario s, is the power sold by the energy storage system to the grid at time t in scenario s, P RES is the rated power of the above renewable energy system, is the power purchased by the load from the grid at time t in scenario s, is the power purchased by the energy storage system from the grid at time t in scenario s, P load_max is the maximum possible value of power demand.

[0063] In the above implementation, the restriction constraints of the first stage are as follows: Equation (17) describes that renewable energy and energy storage systems sell electricity to the system in the day-ahead market, so their rated power should limit the power sold by the virtual power plant. Similarly, from Equation (18), it can be seen that the virtual power plant purchases electricity from the system to supply it to customers and accumulates it in the energy storage system; therefore, the purchasing power of the virtual power plant is limited by the sum of customer demand and the maximum charging power of the energy storage system. The state variable ensures that the virtual power plant cannot buy and sell electricity at the same time in the day-ahead market. In addition, a day-ahead market transaction contract is determined every hour, and each time period in the optimization model is 15 minutes. Therefore, Equations (19) and (20) ensure that the virtual power plant has similar trading capabilities in four 15-minute blocks every hour.

[0064]

[0065]

[0066] in, represents the power sold by renewable energy to the grid at time t in scenario s, represents the power sold by the energy storage system to the grid at time t in scenario s, P RES Represents the rated power of renewable energy, represents the power purchased by the load from the grid at time t in scenario s, represents the power purchased by the energy storage system from the grid at time t in scenario s, P load_max The maximum possible value of a demand.

[0067] In order to consider the restrictions and constraints of the second stage, in an optional implementation, the above step S2022 includes:

[0068] Step S20221, and As the above active power balance constraint condition, is the predicted power demand at time t in scenario s, is the output power of renewable energy to meet the load demand at time t in scenario s, is the output power of the energy storage system to meet the load demand at time t in scenario s;

[0069] Step S20222: and As the operating constraints of the above renewable energy system, is the actual output power of the above renewable energy system at time t in scenario s, P RESmin is the minimum level of output power of the above renewable energy system, is the predicted output power of the above renewable energy system at time t in scenario s, is the upward reserve power of the above renewable energy system at time t in scenario s, represents the downward reserve power of renewable energy at time t in scenario s, is the power used to charge the energy storage system from the renewable energy system at time t in scenario s;

[0070] Step S20223, and As the operating constraints of the above energy storage system, is the power of the energy storage system at the beginning of the day in scenario s, is the electricity of the above energy storage system at the end of the day in scenario s;

[0071] Step S20224, and As the constraint condition of the above reserve energy supply, RE t is the upward reserve capacity in the above balancing energy market contract at time t, It is a binary state variable indicating whether the virtual power plant provides upward reserve in the balancing energy market contract at time t.

[0072] In the above implementation, the second-stage restriction constraints, taking into account the uncertainty of renewable energy and demand, the operating parameters of each part of the virtual power plant and the ability of the virtual power plant in the balancing energy market are described in the second-stage constraints as follows: (a) Active power balancing constraints in the balancing energy market: in, represents the forecast demand at time t in scenario s, Indicates that the renewable energy power output meets the load demand at time t in scenario s, Indicates that the power output of the energy storage system meets the load demand at time t in scenario s. Equation (21) shows that at any time t, the net production / net consumption of the virtual power plant should be equal to the sales / purchase capacity in the day-ahead market contract. Equation (22) shows that renewable energy, energy storage systems or the market can provide power to customers. (b) Operational constraints of renewable energy: in, represents the actual output power of renewable energy at time t in scenario s, P RESmin Indicates the minimum level of power that can be output from renewable energy sources. represents the predicted output power of renewable energy at time t in scenario s, represents the upward reserve power of renewable energy at time t in scenario s, represents the downward reserve power of renewable energy at time t in scenario s, represents the power charged from renewable energy to the energy storage system at time t in scenario s. Formula (23) shows that the upward reserve supply capacity of renewable energy is limited by the predicted available power output and actual output power Similarly, formula (24) shows that the downward reserve capacity limit of renewable energy is the actual output power and the minimum power threshold P RESmin The difference between. It can be seen from equation (25) that renewable energy provides part of the virtual power plant contract in the day-ahead market while meeting the customer's electricity consumption in the virtual power plant. In addition, excess energy from renewable energy can be accumulated in the energy storage system to utilize the available power output of renewable energy. (c) Operation constraints of the energy storage system: The two sets of constraints of the operation model of the energy storage system in the balancing energy market and the reserve market are described as follows: The operating parameters of the energy storage system in the day-ahead market are shown in equations (26)-(29). The relationship between the upward reserve or downward reserve capacity of the energy storage system in the balancing energy market and the dispatch and state switching in the day-ahead market has been described by equations (2)-(15).

[0073]

[0074] in, represents the energy of the energy storage system at the beginning of the day (t=0) in scenario s, represents the energy of the energy storage system at the end of the day (t=96) in scenario s. Equations (26) and (27) limit the charging and discharging power of the energy storage system, and equation (28) describes the change in the energy of the energy storage system after each time period Δt. It can be seen from equation (29) that in order to ensure the operation of the energy storage system on the second day, the energy of the energy storage system should be set to the initial energy level at the end of each day, which is set to 50% of the rated capacity of the energy storage system. (d) Reserve energy supply of virtual power plants in the balancing energy market: The following equation describes the capacity of virtual power plants in the balancing energy market.

[0075]

[0076] Among them, RE t represents the upward reserve capacity requirement in the balancing energy market contract at time t; A binary state variable indicating whether the virtual power plant provides upward reserves in the balancing energy market contract at time t, When the virtual power plant is in the contract, it means that there is a reserve capacity of no less than RE specified in the contract. t From equations (30) and (31), it can be seen that reserve energy can be provided by renewable energy, energy storage systems and customers. Equation (32) shows that the virtual power plant can provide upward or downward reserve energy at other times of the day. The above formula (1) limits the virtual power plant to only one reserve type in the same period.

[0077] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0078] The embodiment of the present application also provides a scheduling device for a virtual power plant. It should be noted that the scheduling device for a virtual power plant in the embodiment of the present application can be used to execute the scheduling method for a virtual power plant provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0079] The following is an introduction to the scheduling device of the virtual power plant provided in the embodiment of the present application.

[0080] Figure 4 is a structural block diagram of a scheduling device for a virtual power plant according to an embodiment of the present application. Figure 4As shown, the device comprises:

[0081] A first establishing unit 10 is used to establish a profit maximization function, wherein the profit maximization function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, wherein the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand;

[0082] A second establishing unit 20 is used to establish constraint conditions for the parameters of the profit maximum function, wherein the parameters of the profit maximum function include the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market;

[0083] A calculation unit 30 is used to calculate the maximum value using the above profit maximum function to obtain the maximum profit of the day when the above constraint conditions are met;

[0084] The dispatching unit 40 is used to dispatch electricity of the virtual power plant according to the parameters of the profit maximization function corresponding to the maximum profit of the day.

[0085] In the dispatching device of the above-mentioned virtual power plant, due to the instability of renewable energy and electricity demand in the virtual power plant, there may be a shortage or surplus in the plan for daily purchase and sale of electricity. This method not only formulates a plan for daily purchase and sale of electricity through the day-ahead market, but also purchases and sells electricity on the same day in the balancing energy market according to the real-time changes in electricity supply and demand to make up for the shortage or surplus, thereby calculating the sum of the profit of the virtual power plant in the day-ahead market and the profit in the balancing energy market, establishing a maximum profit function, and calculating the maximum profit for the day while satisfying the constraints corresponding to the parameters of the maximum profit function. The virtual power plant is dispatched for electricity according to the parameters of the maximum profit function corresponding to the maximum profit for the day, thereby achieving profit maximization, which solves the problem that the dispatching method of the virtual power plant in the prior art is difficult to achieve profit maximization.

[0086] In order to maximize profits, in an optional implementation manner, the first establishing unit includes:

[0087] The first module is used to As the above profit maximization function, A collection of times of the day is the set of scenarios with uncertain parameters, is the day-ahead market price at time t, is the price of upward energy storage at time t, is the price of storing energy downward at time t, Δt is the period of power adjustment, π s is the probability that the scenario at time t is scenario s, p act Balance the probability of energy reserve activation, The sales power at time t in the above day-ahead market is, The purchase power at time t in the above day-ahead market is, At time t in the scenario s, the upward reserve power of the virtual power plant in the balancing energy market is: At time t in the above scenario s, the above virtual power plant reserves downward power in the above balancing energy market.

[0088] In the above implementation, the two-stage scheduling strategy, such as Figure 3As shown in the figure, the first stage is to determine the trading strategy of the day-ahead market, and the second stage is to consider the uncertainty of renewable energy and demand and adjust the unbalanced energy market. Large virtual power plants that integrate multiple renewable energy, energy storage systems and flexible loads are capable of participating in the balancing market, especially when the energy storage system in the virtual power plant accounts for a high proportion of the power structure. The energy storage system has the ability to quickly adjust power, which allows the virtual power plant to supply upward and downward reserves more flexibly. In the first stage, the virtual power plant makes hourly forecasts of the available output power and demand of distributed energy, formulates the corresponding supply and demand plan on the operating day and uploads it to the day-ahead market. The uploaded supply and demand plan cannot be changed in actual production, otherwise it will be punished by the penalty system. The day-ahead market makes reasonable plans for the obtained supply and demand plans and formulates corresponding trading strategies. However, inaccurate forecasts of renewable energy and changes in the number of load users may cause the plan formulated by the day-ahead market to be mismatched with the actual production, resulting in surplus or shortage. At this time, the power system operator needs to balance the market. In the second stage, the virtual power plant receives the latest short-term forecast data of renewable energy and demand. The extremely short-term forecast results are quite accurate, so the virtual power plant can use them as actual data. The virtual power plant determines its trading strategy of upward reserve or downward reserve in the balancing energy market every fifteen minutes. The energy storage system can flexibly increase or decrease the charge and discharge level to achieve mutual conversion between the charging state and the discharging state. It can not only provide upward reserve, but also provide downward reserve, and can obtain more profits while balancing the energy market. It should be noted that the energy storage system is not like other resources that can always have the ability to continuously supply or absorb energy, because when using the energy storage supply capacity, it will not only increase or decrease the charge and discharge power, but also change the energy level stored by the system itself. The two-stage scheduling strategy is to determine the power sales and purchase strategies of the virtual power plant in the day-ahead market when the information about the available power output of renewable energy and customer power consumption is only forecast data. On the basis of the virtual power plant providing contract reserve energy for the balancing energy market, considering the probability of the virtual power plant being selected to provide reserve energy, the backup energy supply capacity of the virtual power plant in the balancing energy market is estimated. By coordinating the operation plan and charging and discharging status of the energy storage system, the total revenue of the virtual power plant in the day-ahead market and the balancing energy market is maximized. In the day-ahead market, the trading strategy of the virtual power plant is a first-stage variable that is determined every hour within 24 hours, and remains unchanged even if the actual data of renewable energy and demand are different from the forecast results. In contrast, the upward or downward reserve energy of the virtual power plant in the balancing energy market, as well as the operating parameters of renewable energy and energy storage systems can be regarded as second-stage variables. Since the time unit of the balancing energy market is usually 15 minutes, the variables in the second stage are determined in quarters of an hour.It is assumed that the day-ahead forecast errors of renewable energy and demand follow a normal distribution with a mean of zero. Therefore, each scenario can be represented by the sum of the forecast value and the random value generated by the forecast error distribution function. The objective function is to maximize the profit of the virtual power plant in the day-ahead market and the balancing energy market within one day. It mainly consists of two parts. The first part is the profit obtained from the day-ahead market, and the second part is the expected profit of selling reserve services in the balancing energy market. Each time period is 15 minutes, with a total of 96 time periods. The profit maximization function can be expressed as.

[0089] In order to consider the state and power limitation of the energy storage system, in an optional implementation manner, the virtual power plant includes an energy storage system, and the device further includes:

[0090] The third establishing unit is used to establish the state constraint conditions of the energy storage system before establishing the constraint conditions for the parameters of the above-mentioned profit maximum function. and in, is a binary state variable that provides the state of the upward reserve at time t under scenario s, is a binary state variable that provides the state of the downward reserve at time t under scenario s, is the binary state variable of the charging and discharging state of the above energy storage system at time t under scenario s, is a binary state variable of the charge and discharge state of the energy storage system after the energy storage system activates the stored energy at time t under scenario s;

[0091] The fourth establishment unit is used to establish the reserve energy form constraint condition and in, Indicates that the above energy storage system provides downward reserve power in the discharge state at time t of scenario s, In the scenario s at time t, the above energy storage system provides downward reserve power in the charging state, Indicates that the above energy storage system provides upward reserve power in the discharge state at time t of scenario s, It represents the upward reserve power provided by the above energy storage system in the charging state at time t of scenario s, and M is a constant;

[0092] The fifth establishment unit is used to establish the upward reserve constraint condition of the energy storage system and Among them, P ESS is the rated power of the above energy storage system;

[0093] The sixth establishment unit is used to establish the downward reserve constraint condition of the energy storage system and

[0094] In the above implementation, the energy storage system plays a vital role in the operation of the energy and reserve market of the virtual power plant. It can compensate for the output power of renewable energy and the consumption fluctuations of users, obtain profits through energy exchange in the energy market, and provide reserve services in the balancing market. However, the energy storage system needs to be reasonably planned so that it can provide better services. The upward reserve and downward reserve that the energy storage system can provide need to be jointly determined based on its own energy level and the day-ahead operation plan. Upward reserve and downward reserve cannot be achieved at the same time. Formula (1) gives the constraint condition in, represents the state of providing upward reserve at time t under scenario s, to provide upward reserve, otherwise 0; Indicates the state of providing downward reserve, To provide downward reserve, otherwise it is 0. The energy storage system usually participates in the energy market in the form of charging and discharging. When providing reserve energy, the energy storage system may be in a charging state or a discharging state, which causes the energy storage system to have multiple state changes. represents the charging and discharging state of the energy storage system at time t under scenario s, =1 indicates the discharge state of the energy storage system, otherwise it is 0; represents the charging and discharging state of the energy storage system after activating the stored energy at time t in scenario s, Indicates the discharge state of the energy storage system after the reserve energy is activated, otherwise it is 0. According to the reserve energy type and the state of the energy storage system before and after the reserve is activated, there are four feasible situations. Formula (2) limits other unachievable states. For example, the energy storage system is normally in a discharge state, and it is required to provide upward reserve. When the energy storage system is in a charging state after the reserve energy is activated, this situation cannot be achieved. Formula (2) is specifically expressed as: In addition, the energy storage system can only provide one form of reserve at the same time, and cannot provide both upward and downward reserves. Formulas (3) and (4) impose relevant constraints on the form of reserve energy provided by the energy storage system, and the big M method is used in the constraints: in, Indicates that the energy storage system provides downward reserve power in the discharge state at time t of scenario s, Indicates that the energy storage system provides downward reserve power in the charging state at time t of scenario s, Indicates that the energy storage system provides upward reserve power in the discharge state at time t of scenario s, represents the upward reserve power provided by the energy storage system in the charging state at time t of scenario s, and M represents a sufficiently large constant. The upward reserve provided by the energy storage system needs to be constrained by actual conditions. Formulas (5)-(8) give the relevant constraints:

[0095] Among them, P ESS Indicates the rated power of the energy storage system. When the energy storage system charging power is 0, the constraint of formula (6) is Formula (7) is not binding, while formula (8) is binding No more than when When, the constraint of formula (6) Formula (8) constraint by restrictions; when When , equations (6) and (8) become unconstrained, and equation (5) becomes Limit to Formula (7) constraint The downward reserve provided by the energy storage system needs to be constrained by actual conditions. Formulas (9)-(12) give the relevant constraints:

[0096]

[0097] when When , equations (10) and (12) become unconstrained, and equation (9) becomes Limit to Formula (11) constraint when When, the constraint of formula (10) Formula (12) constraint by restrictions; when When the energy storage system discharge power is 0, the constraint of formula (10) is Formula (11) is not binding, while formula (12) is binding No more than Assume that under the predetermined operation plan in the energy market, the energy level of the energy storage system after time interval t is After providing upward reserve or downward reserve, the change in energy level of the energy storage system is Specifically expressed as: Among them, η is the charging and discharging efficiency of the energy storage system, and Δt is the duration of the time interval. It can be seen that when the storage is downward Positive value, upward reserve is a negative value. Formula (14) ensures that the reserve energy supply of the energy storage system does not affect its dispatch in the energy market. Formula (15) constrains that the energy level of the energy storage system at any time should be limited by its rated capacity.

[0098] in, represents the energy change of the energy storage system after providing upward reserve or downward reserve at time t = 0 in scenario s, It represents the energy change of the energy storage system after providing upward reserve or downward reserve at t=96 in scenario s, represents the energy of the day-ahead market energy storage system at time t in scenario s, E ESS Indicates the rated capacity of the energy storage system.

[0099] In order to fully consider the constraints of the actual situation, in an optional implementation manner, the virtual power plant further includes a renewable energy system, and the second establishing unit includes:

[0100] The second establishing module is used to establish the constraints of power purchase and power sale in the day-ahead market to obtain a plurality of first constraints, wherein the first constraints include power sale power constraints and power purchase power constraints;

[0101] The third establishment module is used to establish the constraints of upward reserves and downward reserves in the above-mentioned balancing energy market to obtain multiple second constraints. The above-mentioned second constraints include active power balance constraints, operation constraints of the above-mentioned renewable energy system, operation constraints of the above-mentioned energy storage system and constraints on reserve energy supply.

[0102] In the above implementation, the first-stage restriction constraints and the second-stage restriction constraints, i.e., the first constraint conditions and the second constraint conditions, are established. The trading strategy of the virtual power plant in the day-ahead market is completed under the first-stage constraints, and the trading strategy of the virtual power plant in the balancing energy market is completed under the second-stage constraints, so as to be close to the actual scenario and reduce prediction errors.

[0103] In order to consider the restrictions and constraints of the first stage, in an optional implementation, the second establishment module includes:

[0104] The first establishment submodule is used to establish the above-mentioned power selling power constraint conditions And the power purchase constraints in, is the power sold to the grid by the above renewable energy system at time t in scenario s, is the power sold by the energy storage system to the grid at time t in scenario s, P RES is the rated power of the above renewable energy system, is the power purchased by the load from the grid at time t in scenario s, is the power purchased by the energy storage system from the grid at time t in scenario s, P load_max is the maximum possible value of power demand.

[0105] In the above implementation, the restriction constraints of the first stage are as follows: Equation (17) describes that renewable energy and energy storage systems sell electricity to the system in the day-ahead market, so their rated power should limit the power sold by the virtual power plant. Similarly, from Equation (18), it can be seen that the virtual power plant purchases electricity from the system to supply customers and accumulates it in the energy storage system; therefore, the purchasing power of the virtual power plant is limited by the sum of customer demand and the maximum charging power of the energy storage system. The state variable ensures that the virtual power plant cannot buy and sell electricity at the same time in the day-ahead market. In addition, a day-ahead market transaction contract is determined every hour, and each time period in the optimization model is 15 minutes. Therefore, Equations (19) and (20) ensure that the virtual power plant has similar trading capabilities in four 15-minute blocks every hour.

[0106]

[0107]

[0108] in, represents the power sold by renewable energy to the grid at time t in scenario s, represents the power sold by the energy storage system to the grid at time t in scenario s, P RES Represents the rated power of renewable energy, represents the power purchased by the load from the grid at time t in scenario s, represents the power purchased by the energy storage system from the grid at time t in scenario s, P load_max The maximum possible value of a demand.

[0109] In order to consider the restrictions and constraints of the second stage, in an optional implementation, the third establishment module includes:

[0110] The second submodule is used to and As the above active power balance constraint condition, is the predicted power demand at time t in scenario s, is the output power of renewable energy to meet the load demand at time t in scenario s, is the output power of the energy storage system to meet the load demand at time t in scenario s;

[0111] The third submodule is used to and As the operating constraints of the above renewable energy system, is the actual output power of the above renewable energy system at time t in scenario s, P RESmin is the minimum level of output power of the above renewable energy system, is the predicted output power of the above renewable energy system at time t in scenario s, is the upward reserve power of the above renewable energy system at time t in scenario s, represents the downward reserve power of renewable energy at time t in scenario s, is the power used to charge the energy storage system from the renewable energy system at time t in scenario s;

[0112] The fourth submodule is used to

[0113] and As the operating constraints of the above energy storage system, is the power of the energy storage system at the beginning of the day in scenario s, is the electricity of the above energy storage system at the end of the day in scenario s;

[0114] The fifth submodule is used to and As the constraint condition of the above reserve energy supply, RE t is the upward reserve capacity in the above balancing energy market contract at time t, It is a binary state variable indicating whether the virtual power plant provides upward reserve in the balancing energy market contract at time t.

[0115] In the above implementation, the second-stage restriction constraints, taking into account the uncertainty of renewable energy and demand, the operating parameters of each part of the virtual power plant and the ability of the virtual power plant in the balancing energy market are described in the second-stage constraints as follows: (a) Active power balancing constraints in the balancing energy market: in, represents the forecast demand at time t in scenario s, Indicates that the renewable energy power output meets the load demand at time t in scenario s, Indicates that the power output of the energy storage system meets the load demand at time t in scenario s. Equation (21) shows that at any time t, the net production / net consumption of the virtual power plant should be equal to the sales / purchase capacity in the day-ahead market contract. Equation (22) shows that renewable energy, energy storage systems or the market can provide power to customers. (b) Operational constraints of renewable energy:

[0116] in, represents the actual output power of renewable energy at time t in scenario s, P RESmin Indicates the minimum level of power that can be output from renewable energy sources. represents the predicted output power of renewable energy at time t in scenario s, represents the upward reserve power of renewable energy at time t in scenario s, represents the downward reserve power of renewable energy at time t in scenario s, represents the power charged from renewable energy to the energy storage system at time t in scenario s. Formula (23) shows that the upward reserve supply capacity of renewable energy is limited by the predicted available power output and actual output power Similarly, formula (24) shows that the downward reserve capacity limit of renewable energy is the actual output power and the minimum power threshold P RESmin The difference between. It can be seen from equation (25) that renewable energy provides part of the virtual power plant contract in the day-ahead market while meeting the customer's electricity consumption in the virtual power plant. In addition, excess energy from renewable energy can be accumulated in the energy storage system to utilize the available power output of renewable energy. (c) Operation constraints of the energy storage system: The two sets of constraints of the operation model of the energy storage system in the balancing energy market and the reserve market are described as follows: The operating parameters of the energy storage system in the day-ahead market are shown in equations (26)-(29). The relationship between the upward reserve or downward reserve capacity of the energy storage system in the balancing energy market and the dispatch and state switching in the day-ahead market has been described by equations (2)-(15).

[0117]

[0118] in, represents the energy of the energy storage system at the beginning of the day (t=0) in scenario s, represents the energy of the energy storage system at the end of the day (t=96) in scenario s. Equations (26) and (27) limit the charging and discharging power of the energy storage system, and equation (28) describes the change in the energy of the energy storage system after each time period Δt. It can be seen from equation (29) that in order to ensure the operation of the energy storage system on the second day, the energy of the energy storage system should be set to the initial energy level at the end of each day, which is set to 50% of the rated capacity of the energy storage system. (d) Reserve energy supply of virtual power plants in the balancing energy market: The following equation describes the capacity of virtual power plants in the balancing energy market.

[0119]

[0120] Among them, RE t represents the upward reserve capacity requirement in the balancing energy market contract at time t; A binary state variable indicating whether the virtual power plant provides upward reserves in the balancing energy market contract at time t, When the virtual power plant is in the contract, it means that there is a reserve capacity of no less than RE specified in the contract. t From equations (30) and (31), it can be seen that reserve energy can be provided by renewable energy, energy storage systems and customers. Equation (32) shows that the virtual power plant can provide upward or downward reserve energy at other times of the day. The above formula (1) limits the virtual power plant to only one reserve type in the same period.

[0121] The scheduling device of the virtual power plant includes a processor and a memory. The first establishment unit, the second establishment unit, the calculation unit and the scheduling unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0122] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem that the scheduling method of the virtual power plant in the prior art is difficult to maximize the profit is solved by adjusting the kernel parameters.

[0123] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0124] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the scheduling method of the virtual power plant.

[0125] Specifically, the scheduling methods of virtual power plants include:

[0126] Step S201, establishing a maximum profit function, the maximum profit function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand;

[0127] Step S202, establishing constraint conditions for the parameters of the profit maximum function, the parameters of the profit maximum function including the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market;

[0128] Step S203, when the above constraints are met, the above profit maximum function is used to calculate the maximum value to obtain the maximum profit for the day;

[0129] Step S204, dispatching power from the virtual power plant according to the parameters of the maximum profit function corresponding to the maximum profit of the day.

[0130] An embodiment of the present invention provides a processor, which is used to run a program, wherein the scheduling method of the virtual power plant is executed when the program is running.

[0131] Specifically, the scheduling methods of virtual power plants include:

[0132] Step S201, establishing a maximum profit function, the maximum profit function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand;

[0133] Step S202, establishing constraint conditions for the parameters of the profit maximum function, the parameters of the profit maximum function including the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market;

[0134] Step S203, when the above constraints are met, the above profit maximum function is used to calculate the maximum value to obtain the maximum profit for the day;

[0135] Step S204, dispatching power from the virtual power plant according to the parameters of the maximum profit function corresponding to the maximum profit of the day.

[0136] An embodiment of the present invention provides a virtual power plant scheduling system, the virtual power plant scheduling system includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0137] Step S201, establishing a maximum profit function, the maximum profit function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand;

[0138] Step S202, establishing constraint conditions for the parameters of the profit maximum function, the parameters of the profit maximum function including the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market;

[0139] Step S203, when the above constraints are met, the above profit maximum function is used to calculate the maximum value to obtain the maximum profit for the day;

[0140] Step S204, dispatching power from the virtual power plant according to the parameters of the maximum profit function corresponding to the maximum profit of the day.

[0141] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0142] Step S201, establishing a maximum profit function, the maximum profit function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand;

[0143] Step S202, establishing constraint conditions for the parameters of the profit maximum function, the parameters of the profit maximum function including the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market;

[0144] Step S203, when the above constraints are met, the above profit maximum function is used to calculate the maximum value to obtain the maximum profit for the day;

[0145] Step S204, dispatching power from the virtual power plant according to the parameters of the maximum profit function corresponding to the maximum profit of the day.

[0146] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0152] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0153] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0154] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0155] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0156] 1) In the scheduling method of the virtual power plant of the present application, due to the instability of renewable energy and electricity demand in the virtual power plant, there may be a shortage or surplus in the plan for the purchase and sale of electricity on the same day. This method not only makes plans for the purchase and sale of electricity on the same day through the day-ahead market, but also purchases and sells electricity on the same day in the balancing energy market according to the real-time changes in electricity supply and demand to make up for the shortage or surplus, thereby calculating the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market, establishing a maximum profit function, and calculating the maximum profit for the day while satisfying the constraints corresponding to the parameters of the maximum profit function. The virtual power plant is dispatched according to the parameters of the maximum profit function corresponding to the maximum profit for the day, thereby achieving profit maximization, which solves the problem that the scheduling method of the virtual power plant in the prior art is difficult to achieve profit maximization.

[0157] 2) In the dispatching device of the virtual power plant of the present application, due to the instability of renewable energy and electricity demand in the virtual power plant, there may be a shortage or surplus in the plan for the purchase and sale of electricity on the same day. This method not only formulates a plan for the purchase and sale of electricity on the same day through the day-ahead market, but also purchases and sells electricity on the same day in the balancing energy market according to the real-time changes in electricity supply and demand to make up for the shortage or surplus, thereby calculating the sum of the profit of the virtual power plant in the day-ahead market and the profit in the balancing energy market, establishing a maximum profit function, and calculating the maximum profit for the day while satisfying the constraints corresponding to the parameters of the maximum profit function. The virtual power plant is dispatched according to the parameters of the maximum profit function corresponding to the maximum profit for the day, thereby achieving profit maximization, which solves the problem that the dispatching method of the virtual power plant in the prior art is difficult to achieve profit maximization.

[0158] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A scheduling method for a virtual power plant, characterized in that: include: Establishing a profit maximization function, the profit maximization function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand; Establishing constraint conditions for parameters of the profit maximization function, wherein the parameters of the profit maximization function include the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market; Under the condition that the constraint conditions are met, the maximum profit function is used to calculate the maximum value to obtain the maximum profit for the day; The virtual power plant is dispatched for electricity according to the parameters of the profit maximization function corresponding to the maximum profit of the day.

2. The method according to claim 1, characterized in that Establish the profit maximization function, including: Will As the profit maximization function, A collection of times of the day is the set of scenarios with uncertain parameters, is the day-ahead market price at time t, is the price of upward energy storage at time t, is the price of storing energy downward at time t, Δt is the period of power adjustment, π s is the probability that the scenario at time t is scenario s, p act Balance the probability of energy reserve activation, The power sold at time t in the day-ahead market, The purchased power at time t in the day-ahead market is, At time t in the scenario s, the virtual power plant reserves upward power in the balancing energy market, At time t in the scenario s, the virtual power plant reserves power downward in the balancing energy market.

3. The method according to claim 1, characterized in that The virtual power plant includes an energy storage system. Before establishing constraints on the parameters of the profit maximization function, the method further includes: Establishing state constraints for energy storage systems and in, is a binary state variable that provides the state of the upward reserve at time t under scenario s, is a binary state variable that provides the state of the downward reserve at time t under scenario s, is a binary state variable of the charge and discharge state of the energy storage system at time t under scenario s, is a binary state variable of the charge and discharge state of the energy storage system after activating the reserve energy at time t under scenario s; Establishing the constraints of energy reserve form and in, Indicates that the energy storage system provides downward reserve power in the discharge state at time t of scenario s, In the scenario s at time t, the energy storage system provides downward reserve power in the charging state, Indicates that the energy storage system provides upward reserve power in the discharge state at time t of scenario s, represents the upward reserve power provided by the energy storage system in the charging state at time t of scenario s, and M is a constant; Establishing upward reserve constraints for energy storage systems and Among them, P ESS is the rated power of the energy storage system; Establish downward reserve constraints for energy storage systems and 4. The method according to claim 3, characterized in that The virtual power plant also includes a renewable energy system, and constraints are established on the parameters of the profit maximum function, including: Establishing constraints on power purchase and power sale in the day-ahead market, a plurality of first constraints are obtained, wherein the first constraints include power sale power constraints and power purchase power constraints; Based on the constraints of upward reserves and downward reserves in the balancing energy market, multiple second constraints are obtained, and the second constraints include active power balance constraints, operation constraints of the renewable energy system, operation constraints of the energy storage system and reserve energy supply constraints.

5. The method according to claim 4, characterized in that Based on the constraints of power purchase and power sale in the day-ahead market, multiple first constraints are obtained, including: Establish the power selling power constraint condition And the power purchase constraints in, is the power sold to the grid by the renewable energy system at time t in scenario s, is the power sold by the energy storage system to the grid at time t in scenario s, P RES is the rated power of the renewable energy system, is the power purchased by the load from the grid at time t in scenario s, is the power purchased by the energy storage system from the grid at time t in scenario s, P load_max is the maximum possible value of power demand.

6. The method according to claim 4, characterized in that Based on the constraints of upward reserve and downward reserve in the balancing energy market, multiple second constraints are obtained, including: Will and As the active power balance constraint condition, is the predicted power demand at time t in scenario s, is the output power of renewable energy to meet the load demand at time t in scenario s, The output power of the energy storage system at time t in scenario s that meets the load demand; Will and As the operating constraints of the renewable energy system, is the actual output power of the renewable energy system at time t in scenario s, P RESmin is the minimum level of output power of the renewable energy system, is the predicted output power of the renewable energy system at time t in scenario s, is the upward reserve power of the renewable energy system at time t in scenario s, represents the downward reserve power of renewable energy at time t in scenario s, is the power used to charge the energy storage system from the renewable energy system at time t in scenario s; Will and As the operating constraints of the energy storage system, is the power of the energy storage system at the beginning of the day in scenario s, is the amount of electricity of the energy storage system at the end of the day in scenario s; Will and As the constraint condition of the reserve energy supply, RE t is the upward reserve capacity in the balancing energy market contract at time t, It is a binary state variable indicating whether the virtual power plant provides upward reserve in the balancing energy market contract at time t.

7. A virtual power plant dispatching device, characterized in that: include: A first establishing unit is used to establish a profit maximization function, wherein the profit maximization function is used to calculate the maximum value of the sum of the profit of the virtual power plant in the day-ahead market and the profit of the balancing energy market on the same day, wherein the day-ahead market is a market for purchasing and selling electricity on the same day according to a planned plan, and the balancing energy market is a market for purchasing and selling electricity on the same day according to real-time changes in electricity supply and demand; a second establishing unit, configured to establish constraint conditions for parameters of the profit maximization function, wherein the parameters of the profit maximization function include the sales power in the day-ahead market, the purchase power in the day-ahead market, the upward reserve power in the balancing energy market, and the downward reserve power in the balancing energy market; A calculation unit, used to calculate the maximum value using the maximum profit function to obtain the maximum profit of the day when the constraint condition is met; A dispatching unit is used to dispatch electricity from the virtual power plant according to the parameters of the profit maximization function corresponding to the maximum profit of the day.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A virtual power plant dispatching system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 6.

Images