Virtual power plant scheduling method and device based on multi-energy demand response

Abstract

The invention provides a virtual power plant scheduling method and device based on multi-energy demand response, and the method comprises the steps: obtaining historical operation data of a virtual power plant, the historical operation data comprising power grid scheduling parameters and power plant net earnings; constructing a power plant operation model according to the historical operation data, wherein the power plant operation model is used for simulating the change trend of power plant net income along with power grid dispatching parameters; constructing a first constraint group and a second constraint group; and under the limitation of the first constraint group and the second constraint group, solving the power plant operation model by taking the maximum power plant net income as an objective function to obtain a target scheduling parameter, and controlling the virtual power plant to operate according to the target scheduling parameter. The method solves the problem that in the prior art, a virtual power plant scheduling control method which comprehensively considers multiple kinds of energy is lacked.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and in particular to a scheduling method, device, computer-readable storage medium and virtual power plant scheduling system based on multi-energy demand response. Background Art

[0002] To reduce greenhouse gas emissions from the energy sector, distributed generation based on renewable energy (RDER) has gained attention. Wind power WTG and photovoltaic SPVP are the fastest growing technologies among them. Combined heat and power is another more energy-efficient technology. The efficiency of a combined heat and power unit can reach 85-95%, and it also reduces the emission of carbon dioxide and nitrogen oxides. Micro combined heat and power systems are installed at the end-use point, i.e., residential areas, to provide electricity and heat. Hydropower plants (HPPs) are one of the main sources of renewable energy. HPPs convert energy in the most efficient way, and since the cost of generating electricity is very low, it is advantageous to use all available water energy instead of other forms of energy. The use of plug-in electric vehicles (PEVs) has significantly reduced the penetration of internal combustion engine vehicles, which can also reduce air pollution and climate change. Energy management of VPPs is the management of photovoltaic (PV) modules, wind turbines, electrical energy storage systems, combined heat and power units, and pure heat units. VPPs control and coordinate these devices to achieve goals such as minimizing costs and environmental pollution or maximizing benefits. However, analyzing electricity alone is not enough. With the diversification of social energy use methods and the development of electric energy substitution, the scheduling of VPPs including multiple energy sources such as electricity, heating and cooling has become particularly important. Summary of the invention

[0003] The main purpose of this application is to provide a scheduling method, device, computer-readable storage medium and virtual power plant scheduling system for a virtual power plant based on multi-energy demand response, so as to at least solve the problem in the prior art of lack of a virtual power plant scheduling control method that comprehensively considers multiple energy sources.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a scheduling method for a virtual power plant based on multi-energy demand response is provided, including: obtaining historical operating data of the virtual power plant, the historical operating data including grid scheduling parameters and the net profit of the power plant, the grid scheduling parameters at least including electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, unit price of electric energy, unit price of thermal energy and unit price of cooling, cost of wind turbine unit, cost of BMC unit and cost of SMC unit; constructing a power plant operation model based on the historical operation data, the power plant operation model is used to simulate the change of the net profit of the power plant with the change of the grid scheduling parameters trend; construct a first constraint group and a second constraint group, the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraint, thermal energy balance constraint and cold power balance constraint of the virtual power plant, the second constraint group includes at least the power disaster recovery constraint, thermal energy disaster recovery constraint and cold capacity disaster recovery constraint; under the restrictions of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain the target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

[0005] Optionally, constructing a power plant operation model according to the historical operation data includes: determining a first unit price, a second unit price, a third unit price, a first electric energy, a second electric energy, a first thermal energy, a second thermal energy, a first cooling capacity, a second cooling capacity, a first cost, a second cost, a third cost, a first power, a second power and a third power according to the historical operation data, wherein the first unit price is the electric energy unit price, the second unit price is the thermal energy unit price, the third unit price is the cooling capacity unit price, the first electric energy is the predicted power sales volume of the virtual power plant in response to demand, and the second electric energy is the power sold by the virtual power plant to the main power plant. The first heat energy is the predicted heat energy sold by the virtual power plant in response to demand, the second heat energy is the heat energy sold by the virtual power plant to the main grid, the first cooling capacity is the predicted cooling capacity sold by the virtual power plant in response to demand, the second cooling capacity is the cooling capacity sold by the virtual power plant to the main grid, the first cost is the total cost of the wind turbine, the second cost is the total cost of the BMC unit, the third cost is the total cost of the SMC unit, the first power is the power purchased by the virtual power plant from the main grid, and the second power is the The power of thermal energy purchased by the virtual power plant from the main power grid, and the third power is the power of cooling purchased by the virtual power plant from the main power grid; a first target formula is constructed according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity; a second target formula is constructed according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost; the first target formula and the second target formula are combined to obtain the power plant operation model, the first target formula is used to simulate the change trend of the income of the virtual power plant with the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity, and the second target formula is used to simulate the change trend of the expenditure of the virtual power plant with the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost.

[0006] Optionally, a first target formula is constructed according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity; a second target formula is constructed according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost; and the first target formula and the second target formula are combined to obtain the power plant operation model, including: constructing a first target formula according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity: Where R is the revenue of the virtual power plant, f elec,t is the first unit price, f ther,t is the second unit price, f cool,t is the third unit price, P Dt is the first electric energy, is the second electrical energy, H Dt is the first thermal energy, is the second thermal energy, C Dt is the first cooling capacity, is the second cooling capacity, T is the total number of data collection periods; a second target formula is constructed according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost: Where C is the expenditure of the virtual power plant, is the first power, is the second power, is the third power, fw it (Pw it ) is the first cost, fcb it (Pcb it ,Hcb it ) is the second cost, fcs it (Pcs it ,Hcs it )] is the third cost, N W is the total number of wind turbines, N d is the total number of BMC units, N ss is the total number of SMC units; combining the first target formula and the second target formula, the power plant operation model is obtained: Profit = RC; wherein Profit is the net profit of the virtual power plant.

[0007] Optionally, before constructing the second target formula according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost, the method further includes: determining a fourth power, a fifth power, a sixth power, a seventh power and an eighth power according to the historical operation data, the fourth power being the output power of the wind turbine unit of the virtual power plant, the fifth power and the sixth power being the electrical output power and the thermal output power of the BMC unit of the virtual power plant, and the seventh power and the eighth power being the electrical output power and the thermal output power of the SMC unit; constructing a third target formula according to the fourth power, the third target formula being used to simulate the changing trend of the first cost with the fourth power: fw it (Pw it )=Kw i ×P it +Ow it (Pw it )+Uw it (Pw it ), where Kw i is the direct cost coefficient, Ow it (Pw it ) is the standby cost, Uw it (Pw it ) is the penalty cost, Pw it is the fourth power; and constructs a fourth target formula according to the fifth power and the sixth power, wherein the fourth target formula is used to simulate the variation trend of the second cost with the fifth power and the sixth power: Among them, α bci , β bci , γ bci ,δ bci , ε bci and bci is the cost coefficient of the BMC unit, Pcb it is the fifth power, Hcb it is the sixth power; and constructs a fifth target formula according to the seventh power and the eighth power, wherein the fifth target formula is used to simulate the changing trend of the third cost with the seventh power and the eighth power: Among them, α sci , β sci , γ sci ,δ sci , ε sci and sci is the cost coefficient of the SMC unit, Pcs it is the seventh power, Hcs itis the eighth power.

[0008] Optionally, before constructing the third target formula according to the fourth power, the method further includes: constructing a sixth target formula according to the fourth power, the sixth target formula being used to simulate a variation trend of the standby cost with the fourth power: Among them, i is the reserve price coefficient, fw(y)dy is the Weibull probability distribution function of the wind turbine; a seventh target formula is constructed according to the fourth power, and the seventh target formula is used to simulate the change trend of the reserve cost with the fourth power: Among them, uw i is the penalty cost coefficient.

[0009] Optionally, constructing the first constraint group includes: constructing a power balance constraint of the virtual power plant corresponding to the power plant operation model to obtain a first target constraint: Among them, P tra,t is the power exchanged between the virtual power plant and the main grid, Ph it For the generating capacity of hydropower units whose scale is smaller than the preset value, and is the charging and discharging power of the electric energy storage BESS, and The power of the virtual power plant for up / down power demand response, is the power consumption of the electric cooling EC, is the power consumption of the cold storage equipment ISC, is the charging power of the electric vehicle PEV, N EV is the number of electric vehicles; construct the thermal energy balance constraint of the virtual power plant corresponding to the power plant operation model to obtain the second objective constraint: Among them, H tra,t is the power of heat energy exchange between the virtual power plant and the main power grid, and is the charging and discharging power of thermal energy storage TESS, and is the power of the virtual power plant for up / down thermal energy demand response, Hac it is the heat consumption power of the absorption refrigeration unit AC, H Dt is the heat load demand of the virtual power plant; constructing the cooling power constraint of the virtual power plant corresponding to the power plant operation model, and obtaining the third objective constraint: Among them, C tra,t is the power of cold exchange between the virtual power plant and the main power grid, is the cooling power of the absorption refrigeration unit, is the cooling power of the electric refrigeration unit, The cooling power of the ISC equipment is and is the power of the virtual power plant for up / down cooling energy demand response, C Dt is the cooling load demand of the virtual power plant; the first constraint group is obtained by combining the first target constraint, the second target constraint and the third target constraint.

[0010] Optionally, constructing the second constraint group includes: constructing a power disaster recovery constraint of the virtual power plant corresponding to the power plant operation model to obtain a fourth target constraint: in, and are the maximum capacity of up / down adjustment in response to electricity demand, and are binary indicator variables for the up / down adjustment of power demand response respectively; the thermal energy disaster recovery constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain the fifth objective constraint: in, and are the maximum capacity for up / down adjustment in response to thermal energy demand, and is a binary indicator variable for adjusting the thermal energy demand response up / down; a cold capacity disaster recovery model of the virtual power plant corresponding to the power plant operation model is constructed to obtain the sixth objective constraint: in, and are the maximum capacity of up / down adjustment in response to cooling energy demand, and are binary indicator variables for up / down adjustment of cooling energy demand response; the fourth objective constraint, the fifth objective constraint and the sixth objective constraint are combined to obtain the second constraint group.

[0011] According to another aspect of the present application, a scheduling device for a virtual power plant based on multi-energy demand response is provided, the device comprising: a first acquisition unit, used to acquire historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, unit price of electric energy, unit price of thermal energy and unit price of cooling, cost of wind turbine set, cost of BMC set and cost of SMC set; a first construction unit, used to construct a power plant operation model according to the historical operation data, the power plant operation model being used to simulate the net income of the power plant changing with the changing trend of the grid dispatching parameters potential; a second construction unit, used to construct a first constraint group and a second constraint group, the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraint, thermal energy balance constraint and cold power balance constraint of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraint, thermal energy disaster recovery constraint and cold capacity disaster recovery constraint; a calculation unit, used to solve the power plant operation model with the maximum net profit of the power plant as the objective function under the constraints of the first constraint group and the second constraint group to obtain target scheduling parameters, and control the operation of the virtual power plant according to the target scheduling parameters.

[0012] 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.

[0013] 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.

[0014] By applying the technical solution of the present application, in the above-mentioned virtual power plant scheduling method based on multi-energy demand response, first, the historical operation data of the virtual power plant is obtained, the historical operation data includes grid scheduling parameters and the net income of the power plant, the grid scheduling parameters at least include the electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, the unit price of electric energy, the unit price of thermal energy and the unit price of cooling, the cost of wind turbines, the cost of BMC units and the cost of SMC units; then, a power plant operation model is constructed according to the historical operation data, and the power plant operation model is used to simulate the net income of the power plant as the grid scheduling parameters change. trend; then, construct the first constraint group and the second constraint group, the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraint, thermal energy balance constraint and cold power balance constraint of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraint, thermal energy disaster recovery constraint and cold capacity disaster recovery constraint; finally, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain the target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters. The present application provides a scheduling method for a virtual power plant based on multi-energy demand response, which takes into account multiple energy demands such as electricity, heat and cooling, and cooperates with multi-energy demand response to optimize the optimal scheduling mode of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A hardware structure block diagram of a mobile terminal according to a scheduling method of a virtual power plant based on multi-energy demand response provided in an embodiment of the present application is shown;

[0016] Figure 2 A schematic flow chart of a virtual power plant scheduling method based on multi-energy demand response provided according to an embodiment of the present application is shown;

[0017] Figure 3 A structural block diagram of a scheduling device for a virtual power plant based on multi-energy demand response provided according to an embodiment of the present application is shown.

[0018] The above drawings include the following reference numerals:

[0019] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] 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.

[0023] As introduced in the background technology, in order to solve the problem that the prior art lacks a virtual power plant scheduling and control method that comprehensively considers multiple energy sources, the embodiments of the present application provide a virtual power plant scheduling method, device, computer-readable storage medium and virtual power plant scheduling system based on multi-energy demand response.

[0024] 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.

[0025] 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 1 1 is a hardware structure block diagram of a mobile terminal of a scheduling method for a virtual power plant based on multi-energy demand response 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 1Different configurations shown.

[0026] 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 display method of device information 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.

[0027] In this embodiment, a scheduling method for a virtual power plant based on multi-energy demand response 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.

[0028] Figure 2 is a flow chart of a scheduling method for a virtual power plant based on multi-energy demand response according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0029] Step S201, obtaining historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost;

[0030] Specifically, this application takes the maximization of the net profit of the virtual power plant as the ultimate goal of regulation, takes the relevant parameters of the virtual power plant involving electricity, heat energy and cooling as the direct scheduling objects, and realizes the optimal scheduling of multi-energy demand response.

[0031] Step S202, constructing a power plant operation model based on the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income with the grid dispatching parameters;

[0032] Specifically, the above net profit is maximized by optimizing the scheduling of VPP components. Based on the relationship between profit and cost, the correlation between the above grid scheduling parameters and the final net profit is determined, and a corresponding numerical model is established for simulation, thereby obtaining the above power plant operation model.

[0033] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints;

[0034] Specifically, corresponding constraints are established based on the stable operation of the virtual power plant in a normal state and the disaster recovery of the virtual power plant under various unexpected events, thereby obtaining the above-mentioned first constraint group and the above-mentioned second constraint group.

[0035] Step S204, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

[0036] Specifically, the net profit maximization of the above power plant operation model is taken as the objective function, and it is solved under the above constraints to obtain different input parameters of the above power plant operation model, that is, the above target scheduling parameters are obtained, and the clearing and entry of the above virtual power plant are completed according to the above target scheduling parameters.

[0037] Through this embodiment, first, the historical operation data of the virtual power plant is obtained, the historical operation data includes the grid dispatching parameters and the net income of the power plant, the grid dispatching parameters include at least the electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, the unit price of electric energy, the unit price of thermal energy and the unit price of cooling, the cost of wind turbines, the cost of BMC units and the cost of SMC units; then, a power plant operation model is constructed according to the historical operation data, and the power plant operation model is used to simulate the changing trend of the net income of the power plant with the grid dispatching parameters; thereafter, a first constraint group and a second constraint group are constructed, the first constraint group includes the operation parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cooling power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cooling disaster recovery constraints; finally, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net income of the power plant as the objective function to obtain the target dispatching parameters, and the operation of the virtual power plant is controlled according to the target dispatching parameters. The present application provides a scheduling method for a virtual power plant based on multi-energy demand response, which takes into account multiple energy demands such as electricity, heat and cooling, and cooperates with multi-energy demand response to optimize the optimal scheduling mode of the system.

[0038] In order to construct the above power plant operation model, in an optional implementation, the above step S202 includes:

[0039] Step S2021, according to the above historical operation data, determine the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity, the second cooling capacity, the first cost, the second cost, the third cost, the first power, the second power and the third power, the first unit price is the above electric energy unit price, the second unit price is the above thermal energy unit price, the third unit price is the above cooling capacity unit price, the first electric energy is the predicted electricity sales amount of the above virtual power plant in response to demand, the second electric energy is the electricity sales amount sold by the above virtual power plant to the above main power grid, and the first thermal energy is the predicted thermal energy sales amount of the above virtual power plant in response to demand. The second heat energy is the heat energy sold by the virtual power plant to the main power grid, the first cooling capacity is the predicted cooling capacity sold by the virtual power plant for demand response, the second cooling capacity is the cooling capacity sold by the virtual power plant to the main power grid, the first cost is the total cost of the wind turbine set, the second cost is the total cost of the BMC set, the third cost is the total cost of the SMC set, the first power is the power of the virtual power plant purchasing electric energy from the main power grid, the second power is the power of the virtual power plant purchasing heat energy from the main power grid, and the third power is the power of the virtual power plant purchasing cooling capacity from the main power grid;

[0040] Specifically, the unit price of electricity of the virtual power plant is determined based on the above historical operation data. elec,t , get the first unit price, the heat unit price f ther,t , we get the second unit price, cooling unit price f cool,t , obtain the third unit price, the predicted power sales of the virtual power plant in response to demand, obtain the first electric energy, and the power sales of the virtual power plant to the main power grid The second electric energy is obtained, and the predicted heat energy sold by the virtual power plant for demand response is H Dt , obtain the first heat energy, the heat energy sold by the virtual power plant to the main power grid The second heat energy is obtained, and the virtual power plant predicts the sales of cooling capacity C in response to demand. Dt , the first cooling capacity is obtained, and the cooling capacity sold by the virtual power plant to the main power grid is The second cooling capacity is obtained, and the total cost of the wind turbine is fw it (Pw it ), we get the first cost mentioned above, the total cost of the BMC unit fcb it (Pcb it ,Hcb it ), the second cost mentioned above is obtained, and the total cost of the SMC unit is fcs it (Pcs it ,Hcs it )], the third cost is obtained, the power of the virtual power plant purchasing electricity from the main grid The virtual power plant purchases the thermal energy from the main power grid to obtain the second power. The virtual power plant purchases cooling power from the main grid The above-mentioned third power is obtained.

[0041] Step S2022, constructing a first target formula according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity; constructing a second target formula according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost; combining the first target formula and the second target formula to obtain the power plant operation model; the first target formula is used to simulate the revenue of the virtual power plant changing with the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity; the second target formula is used to simulate the expenditure of the virtual power plant changing with the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost.

[0042] Specifically, the above-mentioned grid dispatching parameters and the correlation relationship between the net income of the power plant corresponding to the virtual power plant are constructed respectively to obtain the corresponding formula.

[0043] In order to construct the above formulas of the power plant operation model, in an optional implementation, the above step S202 includes:

[0044] Step S2021, constructing a first target formula according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity:

[0045]

[0046] Among them, R is the revenue of the virtual power plant, f elec,t is the first unit price mentioned above, f ther,t is the second unit price mentioned above, f cool,t is the third unit price mentioned above, P Dt is the first electric energy mentioned above, is the second electrical energy mentioned above, H Dt is the first thermal energy mentioned above, is the second thermal energy mentioned above, C Dt is the first cooling capacity mentioned above, is the second cooling capacity mentioned above, T is the total number of data collection periods;

[0047] Step S2022: construct a second target formula according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost:

[0048]

[0049] Among them, C is the expenditure of the above virtual power plant, is the first power mentioned above, is the second power mentioned above, is the third power mentioned above, fw it (Pw it ) is the first cost mentioned above, fcb it (Pcb it ,Hcb it ) is the second cost mentioned above, fcs it (Pcs it ,Hcs it )] is the third cost mentioned above, N W is the total number of wind turbines, N d is the total number of BMC units, N ss is the total number of SMC units;

[0050] Step S2023, combining the first target formula and the second target formula to obtain the power plant operation model:

[0051] Profit = RC;

[0052] Among them, Profit is the net income of the above-mentioned virtual power plant.

[0053] In the above embodiment, the revenue of the virtual power plant comes from the electricity, heat and cold sold to consumers and the main grid, and the cost mainly includes the cost of purchasing electricity, heat and cold from the main grid, the operation and maintenance cost of wind turbines, solar micro-cogeneration units SMC and biofuel micro-turbines BMC units. Since the operating cost of small hydropower stations is very small, it is set to 0 in this application. According to the above revenue and cost, corresponding formulas are constructed to determine the net revenue of the above power plant operation model as the above grid dispatch parameters change.

[0054] In order to determine the total cost of the wind turbine set, the BMC set and the SMC set, in an optional implementation manner, before constructing the second target formula according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost, the method further includes:

[0055] Step S301, determining a fourth power, a fifth power, a sixth power, a seventh power and an eighth power according to the historical operation data, wherein the fourth power is the output power of the wind turbine unit of the virtual power plant, the fifth power and the sixth power are the electric energy output power and the thermal energy output power of the BMC unit of the virtual power plant, and the seventh power and the eighth power are the electric energy output power and the thermal energy output power of the SMC unit;

[0056] Specifically, the output power Pw of the wind turbine group of the virtual power plant is determined according to the historical operation data. it , the fourth power is obtained, the electrical output power and thermal output power Pcb of the BMC unit of the virtual power plant it and Hcb it , the fifth power and the sixth power are obtained, and the electrical output power and thermal output power Pcs of the SMC unit of the virtual power plant are obtained. it and Hcs it , and obtain the seventh power and the eighth power.

[0057] Step S302: construct a third target formula according to the fourth power, wherein the third target formula is used to simulate the variation trend of the first cost with the fourth power:

[0058] f it (Pw it )=Kw i ×P it +Ow it (Pw it )+Uw it (Pw it );

[0059] Among them, Kw i is the direct cost coefficient, Ow it (Pw it ) is the standby cost, Uw it (Pw it ) is the penalty cost, Pw it is the fourth power mentioned above;

[0060] Step S303: construct a fourth target formula according to the fifth power and the sixth power, wherein the fourth target formula is used to simulate the variation trend of the second cost with the fifth power and the sixth power:

[0061]

[0062] Among them, α bci , β bci , γ bci ,δ bci , εbci and bci is the cost coefficient of the above BMC unit, Pcb it is the fifth power mentioned above, Hcb it is the sixth power mentioned above;

[0063] Step S304: construct a fifth target formula according to the seventh power and the eighth power. The fifth target formula is used to simulate the variation trend of the third cost with the seventh power and the eighth power:

[0064]

[0065] Among them, α sci , β sci , γ sci ,δ sci , ε sci and sci is the cost coefficient of the above SMC unit, Pcs it For the seventh power, Hcs it It is the eighth power mentioned above.

[0066] In the above embodiments, the present application constructs corresponding formulas for the relationship between the cost and power generation of wind turbines, the cost and power generation and heating power of BMC units, and the power generation and heating power of SMC units, respectively, to determine the cost changes of wind turbines, SMC units and BMC units as the grid dispatching parameters change.

[0067] In order to determine the standby cost and penalty cost of the wind turbine generator set, in an optional implementation manner, before constructing the third target formula according to the fourth power, the method further includes:

[0068] Step S401, constructing a sixth target formula according to the fourth power, wherein the sixth target formula is used to simulate the variation trend of the standby cost with the fourth power:

[0069]

[0070] Among them, i is the reserve price coefficient, fw(y)dy is the Weibull probability distribution function of the above wind turbine;

[0071] Step S402: construct a seventh target formula according to the fourth power, and the seventh target formula is used to simulate the variation trend of the standby cost with the fourth power:

[0072]

[0073] Among them, uw i is the penalty cost coefficient.

[0074] In the above embodiments, the present application constructs corresponding formulas for the relationship between the standby cost and power generation of the above wind turbines and the penalty cost and power generation of the above wind turbines, respectively, to determine the change of the standby cost and penalty cost of the wind turbines with the grid dispatching parameters.

[0075] In order to construct the first constraint group, in an optional implementation, the step S203 includes:

[0076] Step S2031, constructing the power balance constraint of the virtual power plant corresponding to the power plant operation model, and obtaining the first target constraint:

[0077]

[0078] Among them, P tra,t is the power exchanged between the virtual power plant and the main power grid, Ph it For the generating capacity of hydropower units whose scale is smaller than the preset value, and is the charging and discharging power of the electric energy storage BESS, and is the power of the above virtual power plant for up / down power demand response, is the power consumption of the electric cooling EC, is the power consumption of the cold storage equipment ISC, is the charging power of the electric vehicle PEV, N EV is the number of electric vehicles;

[0079] Specifically, the power generation of a small hydropower station is a polynomial function of the turbine discharge and the reservoir storage capacity, as follows:

[0080]

[0081] Among them, C 1i , C 2i , C 3i , C 4i , C 5i , C 6i is the power generation coefficient, Vh it is the water discharge rate, Qh it It is the water volume of the reservoir.

[0082] Furthermore, the physical limitations of reservoir storage and discharge rates and the continuity formula for the reservoir system are as follows:

[0083]

[0084] in, and The minimum and maximum discharge rates, and Minimum and maximum reservoir storage capacity, Ih it and Sh it are the inflow rate and overflow rate of the reservoir, respectively.

[0085] In addition, the output power of the wind turbine generator set mainly depends on the wind speed. The power output formula is as follows:

[0086]

[0087] Among them, Vf t To predict wind speed, V in , V out is the cut-in wind speed and cut-out wind speed, Vr t is the rated wind speed, A w , B w and C w is the coefficient, Pwr i is the rated power.

[0088] Step S2032: construct the thermal energy balance constraint of the virtual power plant corresponding to the power plant operation model to obtain the second target constraint:

[0089]

[0090] Among them, H tra,t is the power of heat energy exchange between the virtual power plant and the main power grid, and is the charging and discharging power of thermal energy storage TESS, and is the power of the virtual power plant for up / down thermal energy demand response, Hac it is the heat consumption power of the absorption refrigeration unit AC, H Dt is the heat load demand of the above virtual power plant;

[0091] Step S2033, construct the cooling power constraint of the virtual power plant corresponding to the power plant operation model to obtain the third target constraint:

[0092]

[0093] Among them, C tra,t is the power of cooling exchange between the virtual power plant and the main power grid, is the cooling power of the absorption refrigeration unit, is the cooling power of the electric refrigeration unit, The cooling power of the ISC equipment is and is the power of the virtual power plant up / down cooling energy demand response, C Dt is the cooling load demand of the above virtual power plant.

[0094] Step S2034: Combining the first objective constraint, the second objective constraint and the third objective constraint to obtain the first constraint group.

[0095] In order to construct the second constraint group, in an optional implementation, the step S203 further includes:

[0096] Step S2035, construct the power disaster recovery constraint of the virtual power plant corresponding to the power plant operation model, and obtain the fourth target constraint:

[0097]

[0098] in, and are the maximum capacity of up / down adjustment in response to electricity demand, and are binary indicator variables for up / down adjustment of electricity demand response, respectively;

[0099] Specifically, the demand response constraint formula for the electric DRP is given as above.

[0100] Step S2036, construct the thermal energy disaster recovery constraint of the virtual power plant corresponding to the power plant operation model, and obtain the fifth target constraint:

[0101]

[0102] in, and are the maximum capacity for up / down adjustment in response to thermal energy demand, and A binary indicator variable for up / down adjustment of heat demand response;

[0103] Specifically, the demand response constraint formula for thermal DRP is given as above.

[0104] Step S2037, construct a cooling capacity disaster recovery model of the virtual power plant corresponding to the power plant operation model, and obtain the sixth objective constraint:

[0105]

[0106] in, and are the maximum capacity of up / down adjustment in response to cooling energy demand, and are binary indicator variables for up / down adjustment of cooling energy demand response, respectively;

[0107] Step S2038, combining the fourth objective constraint, the fifth objective constraint and the sixth objective constraint to obtain the second constraint group.

[0108] Specifically, the demand response constraint formula for cold DRP is given as above.

[0109] 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.

[0110] The embodiment of the present application also provides a scheduling device for a virtual power plant based on multi-energy demand response. It should be noted that the scheduling device for a virtual power plant based on multi-energy demand response in the embodiment of the present application can be used to execute the scheduling method for a virtual power plant based on multi-energy demand response 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 device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0111] The following introduces the scheduling device of the virtual power plant based on multi-energy demand response provided in the embodiment of the present application.

[0112] Figure 3 is a structural block diagram of a scheduling device for a virtual power plant based on multi-energy demand response according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0113] A first acquisition unit 10 is used to acquire historical operation data of the virtual power plant, wherein the historical operation data includes grid dispatching parameters and net income of the power plant, wherein the grid dispatching parameters include at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost;

[0114] Specifically, this application takes the maximization of the net profit of the virtual power plant as the ultimate goal of regulation, takes the relevant parameters of the virtual power plant involving electricity, heat energy and cooling as the direct scheduling objects, and realizes the optimal scheduling of multi-energy demand response.

[0115] A first construction unit 20 is used to construct a power plant operation model according to the historical operation data, wherein the power plant operation model is used to simulate the change trend of the net income of the power plant with the grid dispatching parameters;

[0116] Specifically, the above net profit is maximized by optimizing the scheduling of VPP components. Based on the relationship between profit and cost, the correlation between the above grid scheduling parameters and the final net profit is determined, and a corresponding numerical model is established for simulation, thereby obtaining the above power plant operation model.

[0117] A second construction unit 30 is used to construct a first constraint group and a second constraint group, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints;

[0118] Specifically, corresponding constraints are established based on the stable operation of the virtual power plant in a normal state and the disaster recovery of the virtual power plant under various unexpected events, thereby obtaining the above-mentioned first constraint group and the above-mentioned second constraint group.

[0119] The calculation unit 40 is used to solve the power plant operation model under the constraints of the first constraint group and the second constraint group, with the maximum net profit of the power plant as the objective function, to obtain target scheduling parameters, and control the operation of the virtual power plant according to the target scheduling parameters.

[0120] Specifically, the net profit maximization of the above power plant operation model is taken as the objective function, and it is solved under the above constraints to obtain different input parameters of the above power plant operation model, that is, the above target scheduling parameters are obtained, and the clearing and entry of the above virtual power plant are completed according to the above target scheduling parameters.

[0121] Through this embodiment, the first acquisition unit acquires the historical operation data of the virtual power plant, the historical operation data includes the grid dispatching parameters and the net income of the power plant, the grid dispatching parameters include at least the electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, the unit price of electric energy, the unit price of thermal energy and the unit price of cooling, the cost of wind turbines, the cost of BMC units and the cost of SMC units; the first construction unit constructs the power plant operation model according to the historical operation data, and the power plant operation model is used to simulate the changing trend of the net income of the power plant with the grid dispatching parameters; the second construction unit constructs the first constraint group and the second constraint group, the first constraint group includes the operation parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraint, thermal energy balance constraint and cooling power balance constraint of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraint, thermal energy disaster recovery constraint and cooling disaster recovery constraint; the calculation unit solves the power plant operation model with the maximum net income of the power plant as the objective function under the constraints of the first constraint group and the second constraint group to obtain the target dispatching parameters, and controls the operation of the virtual power plant according to the target dispatching parameters. The present application provides a scheduling method for a virtual power plant based on multi-energy demand response, which takes into account multiple energy demands such as electricity, heat and cooling, and cooperates with multi-energy demand response to optimize the optimal scheduling mode of the system.

[0122] The above-mentioned virtual power plant scheduling device based on multi-energy demand response includes a processor and a memory, and the above-mentioned first acquisition unit, first construction unit, second construction unit and calculation element are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[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 based on multi-energy demand response.

[0125] Specifically, the scheduling method of a virtual power plant based on multi-energy demand response includes:

[0126] Step S201, obtaining historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost;

[0127] Specifically, this application takes the maximization of the net profit of the virtual power plant as the ultimate goal of regulation, takes the relevant parameters of the virtual power plant involving electricity, heat energy and cooling as the direct scheduling objects, and realizes the optimal scheduling of multi-energy demand response.

[0128] Step S202, constructing a power plant operation model based on the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income with the grid dispatching parameters;

[0129] Specifically, the above net profit is maximized by optimizing the scheduling of VPP components. Based on the relationship between profit and cost, the correlation between the above grid scheduling parameters and the final net profit is determined, and a corresponding numerical model is established for simulation, thereby obtaining the above power plant operation model.

[0130] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints;

[0131] Specifically, corresponding constraints are established based on the stable operation of the virtual power plant in a normal state and the disaster recovery of the virtual power plant under various unexpected events, thereby obtaining the above-mentioned first constraint group and the above-mentioned second constraint group.

[0132] Step S204, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

[0133] Specifically, the net profit maximization of the above power plant operation model is taken as the objective function, and it is solved under the above constraints to obtain different input parameters of the above power plant operation model, that is, the above target scheduling parameters are obtained, and the clearing and entry of the above virtual power plant are completed according to the above target scheduling parameters.

[0134] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program, when running, executes the scheduling method of the virtual power plant based on multi-energy demand response.

[0135] Specifically, the scheduling method of a virtual power plant based on multi-energy demand response includes:

[0136] Step S201, obtaining historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost;

[0137] Specifically, this application takes the maximization of the net profit of the virtual power plant as the ultimate goal of regulation, takes the relevant parameters of the virtual power plant involving electricity, heat energy and cooling as the direct scheduling objects, and realizes the optimal scheduling of multi-energy demand response.

[0138] Step S202, constructing a power plant operation model based on the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income with the grid dispatching parameters;

[0139] Specifically, the above net profit is maximized by optimizing the scheduling of VPP components. Based on the relationship between profit and cost, the correlation between the above grid scheduling parameters and the final net profit is determined, and a corresponding numerical model is established for simulation, thereby obtaining the above power plant operation model.

[0140] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints;

[0141] Specifically, corresponding constraints are established based on the stable operation of the virtual power plant in a normal state and the disaster recovery of the virtual power plant under various unexpected events, thereby obtaining the above-mentioned first constraint group and the above-mentioned second constraint group.

[0142] Step S204, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

[0143] Specifically, the net profit maximization of the above power plant operation model is taken as the objective function, and it is solved under the above constraints to obtain different input parameters of the above power plant operation model, that is, the above target scheduling parameters are obtained, and the clearing and entry of the above virtual power plant are completed according to the above target scheduling parameters.

[0144] 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:

[0145] Step S201, obtaining historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost;

[0146] Specifically, this application takes the maximization of the net profit of the virtual power plant as the ultimate goal of regulation, takes the relevant parameters of the virtual power plant involving electricity, heat energy and cooling as the direct scheduling objects, and realizes the optimal scheduling of multi-energy demand response.

[0147] Step S202, constructing a power plant operation model based on the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income with the grid dispatching parameters;

[0148] Specifically, the above net profit is maximized by optimizing the scheduling of VPP components. Based on the relationship between profit and cost, the correlation between the above grid scheduling parameters and the final net profit is determined, and a corresponding numerical model is established for simulation, thereby obtaining the above power plant operation model.

[0149] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints;

[0150] Specifically, corresponding constraints are established based on the stable operation of the virtual power plant in a normal state and the disaster recovery of the virtual power plant under various unexpected events, thereby obtaining the above-mentioned first constraint group and the above-mentioned second constraint group.

[0151] Step S204, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

[0152] Specifically, the net profit maximization of the above power plant operation model is taken as the objective function, and it is solved under the above constraints to obtain different input parameters of the above power plant operation model, that is, the above target scheduling parameters are obtained, and the clearing and entry of the above virtual power plant are completed according to the above target scheduling parameters.

[0153] 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:

[0154] Step S201, obtaining historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost;

[0155] Specifically, this application takes the maximization of the net profit of the virtual power plant as the ultimate goal of regulation, takes the relevant parameters of the virtual power plant involving electricity, heat energy and cooling as the direct scheduling objects, and realizes the optimal scheduling of multi-energy demand response.

[0156] Step S202, constructing a power plant operation model based on the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income with the grid dispatching parameters;

[0157] Specifically, the above net profit is maximized by optimizing the scheduling of VPP components. Based on the relationship between profit and cost, the correlation between the above grid scheduling parameters and the final net profit is determined, and a corresponding numerical model is established for simulation, thereby obtaining the above power plant operation model.

[0158] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints;

[0159] Specifically, corresponding constraints are established based on the stable operation of the virtual power plant in a normal state and the disaster recovery of the virtual power plant under various unexpected events, thereby obtaining the above-mentioned first constraint group and the above-mentioned second constraint group.

[0160] Step S204, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

[0161] Specifically, the net profit maximization of the above power plant operation model is taken as the objective function, and it is solved under the above constraints to obtain different input parameters of the above power plant operation model, that is, the above target scheduling parameters are obtained, and the clearing and entry of the above virtual power plant are completed according to the above target scheduling parameters.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0167] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0168] 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.

[0169] 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 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.

[0170] 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.

[0171] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0172] 1) The scheduling method of the virtual power plant based on multi-energy demand response of the present application, first, obtain the historical operation data of the virtual power plant, the historical operation data includes the grid scheduling parameters and the net income of the power plant, the grid scheduling parameters at least include the electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, the unit price of electric energy, the unit price of thermal energy and the unit price of cooling, the cost of wind turbines, the cost of BMC units and the cost of SMC units; then, build a power plant operation model based on the historical operation data, the power plant operation model is used to simulate the change trend of the net income of the power plant with the grid scheduling parameters; Afterwards, a first constraint group and a second constraint group are constructed, wherein the first constraint group includes the operating parameter constraints of the power plant operation model, and the second constraint group includes the disaster recovery constraints of the power plant operation model. The first constraint group includes at least the power balance constraint, thermal energy balance constraint and cold power balance constraint of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraint, thermal energy disaster recovery constraint and cold capacity disaster recovery constraint; finally, under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain the target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters. The present application provides a scheduling method for a virtual power plant based on multi-energy demand response, which takes into account multiple energy demands such as electricity, heat and cooling, and cooperates with multi-energy demand response to optimize the optimal scheduling mode of the system.

[0173] 2) The scheduling device of the virtual power plant based on multi-energy demand response of the present application, the first acquisition unit acquires the historical operation data of the virtual power plant, the historical operation data includes the grid scheduling parameters and the net income of the power plant, the grid scheduling parameters at least include the electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, the unit price of electric energy, the unit price of thermal energy and the unit price of cooling, the cost of wind turbines, the cost of BMC units and the cost of SMC units; the first construction unit constructs a power plant operation model according to the historical operation data, and the power plant operation model is used to simulate the changing trend of the net income of the power plant with the grid scheduling parameters ; The second construction unit constructs a first constraint group and a second constraint group, the first constraint group includes the operating parameter constraints of the power plant operation model, the second constraint group includes the disaster recovery constraints of the power plant operation model, the first constraint group includes at least the power balance constraint, thermal energy balance constraint and cold power balance constraint of the virtual power plant, and the second constraint group includes at least the power disaster recovery constraint, thermal energy disaster recovery constraint and cold capacity disaster recovery constraint; the calculation unit solves the power plant operation model under the constraints of the first constraint group and the second constraint group with the maximum net profit of the power plant as the objective function to obtain the target scheduling parameters, and controls the operation of the virtual power plant according to the target scheduling parameters. The present application provides a scheduling method for a virtual power plant based on multi-energy demand response, which takes into account multiple energy demands such as electricity, heat and cooling, and cooperates with multi-energy demand response to optimize the optimal scheduling mode of the system.

[0174] 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 based on multi-energy demand response, characterized in that: include: Acquire historical operation data of the virtual power plant, the historical operation data including grid dispatching parameters and net income of the power plant, the grid dispatching parameters including at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, unit price of electric energy, unit price of thermal energy and unit price of cooling, cost of wind turbine unit, cost of BMC unit and cost of SMC unit; Constructing a power plant operation model according to the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income along with the grid dispatching parameters; Constructing a first constraint group and a second constraint group, wherein the first constraint group includes operating parameter constraints of the power plant operating model, the second constraint group includes disaster recovery constraints of the power plant operating model, the first constraint group includes at least power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints; Under the constraints of the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and the operation of the virtual power plant is controlled according to the target scheduling parameters.

2. The method according to claim 1, characterized in that: Constructing a power plant operation model according to the historical operation data, including: The first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity, the second cooling capacity, the first cost, the second cost, the third cost, the first power, the second power and the third power are determined according to the historical operation data, wherein the first unit price is the electric energy unit price, the second unit price is the thermal energy unit price, the third unit price is the cooling capacity unit price, the first electric energy is the predicted electricity sales amount of the virtual power plant in response to demand, the second electric energy is the electricity sales amount sold by the virtual power plant to the main power grid, the first thermal energy is the predicted thermal energy sales amount of the virtual power plant in response to demand, The second heat energy is the amount of heat energy sold by the virtual power plant to the main power grid, the first cooling capacity is the predicted cooling capacity sold by the virtual power plant for demand response, the second cooling capacity is the cooling capacity sold by the virtual power plant to the main power grid, the first cost is the total cost of the wind turbine unit, the second cost is the total cost of the BMC unit, the third cost is the total cost of the SMC unit, the first power is the power of the virtual power plant purchasing electric energy from the main power grid, the second power is the power of the virtual power plant purchasing heat energy from the main power grid, and the third power is the power of the virtual power plant purchasing cooling from the main power grid; A first target formula is constructed according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity; a second target formula is constructed according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost; the first target formula and the second target formula are combined to obtain the power plant operation model; the first target formula is used to simulate the change trend of the revenue of the virtual power plant with the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity and the second cooling capacity; the second target formula is used to simulate the change trend of the expenditure of the virtual power plant with the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost and the third cost.

3. The method according to claim 2, characterized in that A first target formula is constructed according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity, and the second cooling capacity; a second target formula is constructed according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost, and the third cost; and the first target formula and the second target formula are combined to obtain the power plant operation model, including: A first target formula is constructed according to the first unit price, the second unit price, the third unit price, the first electric energy, the second electric energy, the first thermal energy, the second thermal energy, the first cooling capacity, and the second cooling capacity: Where R is the revenue of the virtual power plant, f elec,t is the first unit price, f ther,t is the second unit price, f cool,t is the third unit price, P Dt is the first electric energy, is the second electrical energy, H Dt is the first thermal energy, is the second thermal energy, C Dt is the first cooling capacity, is the second cooling capacity, T is the total number of data collection periods; A second target formula is constructed according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost, and the third cost: Where C is the expenditure of the virtual power plant, is the first power, is the second power, is the third power, fw it (Pw it ) is the first cost, fcb it (Pcb it ,Hcb it ) is the second cost, fcs it (Pcs it ,Hcs it )] is the third cost, N W is the total number of wind turbines, N d is the total number of BMC units, N ss is the total number of SMC units; The first target formula and the second target formula are combined to obtain the power plant operation model: Profit = RC; Among them, Profit is the net income of the virtual power plant.

4. The method according to claim 3, characterized in that Before constructing a second target formula according to the first unit price, the second unit price, the third unit price, the first power, the second power, the third power, the first cost, the second cost, and the third cost, the method further includes: Determine a fourth power, a fifth power, a sixth power, a seventh power and an eighth power according to the historical operation data, wherein the fourth power is the output power of the wind turbine unit of the virtual power plant, the fifth power and the sixth power are the electric energy output power and the thermal energy output power of the BMC unit of the virtual power plant, and the seventh power and the eighth power are the electric energy output power and the thermal energy output power of the SMC unit; A third target formula is constructed according to the fourth power, and the third target formula is used to simulate the variation trend of the first cost with the fourth power: fw it (Pw it )=Kw i ×Pw it +Ow it (Pw it )+Your it (Pw it ); Among them, Kw i is the direct cost coefficient, Ow it (Pw it ) is the standby cost, Uw it (Pw it ) is the penalty cost, Pw it is the fourth power; A fourth target formula is constructed according to the fifth power and the sixth power, and the fourth target formula is used to simulate the variation trend of the second cost with the fifth power and the sixth power: Among them, α bci , β bci , γ bci ,δ bci , ε bci and bci is the cost coefficient of the BMC unit, Pcb it is the fifth power, Hcb it is the sixth power; A fifth target formula is constructed according to the seventh power and the eighth power, and the fifth target formula is used to simulate the variation trend of the third cost with the seventh power and the eighth power: Among them, α sci , β sci , γ sci ,δ sci , ε sci and sci is the cost coefficient of the SMC unit, Pcs it is the seventh power, Hcs it is the eighth power.

5. The method according to claim 4, characterized in that Before constructing the third target formula according to the fourth power, the method further includes: A sixth target formula is constructed according to the fourth power, and the sixth target formula is used to simulate the variation trend of the standby cost with the fourth power: Among them, i is the reserve price coefficient, fw(y)dy is the Weibull probability distribution function of the wind turbine; A seventh target formula is constructed according to the fourth power, and the seventh target formula is used to simulate the variation trend of the standby cost with the fourth power: Among them, uw i is the penalty cost coefficient.

6. The method according to claim 1, characterized in that Construct the first constraint group, including: Construct the power balance constraint of the virtual power plant corresponding to the power plant operation model to obtain the first target constraint: Among them, P tra,t is the power exchanged between the virtual power plant and the main grid, Ph it For the generating capacity of hydropower units whose scale is smaller than the preset value, and is the charging and discharging power of the electric energy storage BESS, and The power of the virtual power plant for up / down power demand response, is the power consumption of the electric cooling EC, is the power consumption of the cold storage equipment ISC, is the charging power of the electric vehicle PEV, N EV is the number of electric vehicles; The thermal energy balance constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain the second objective constraint: Among them, H tra,t is the power of heat energy exchange between the virtual power plant and the main power grid, and is the charging and discharging power of thermal energy storage TESS, and is the power of the virtual power plant for up / down thermal energy demand response, Hac it is the heat consumption power of the absorption refrigeration unit AC, H Dt is the heat load demand of the virtual power plant; The cooling power constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain the third objective constraint: Among them, C tra,t is the power of cold exchange between the virtual power plant and the main power grid, is the cooling power of the absorption refrigeration unit, is the cooling power of the electric refrigeration unit, The cooling power of the ISC equipment is and is the power of the virtual power plant for up / down cooling energy demand response, C Dt is the cooling load demand of the virtual power plant; The first constraint group is obtained by combining the first objective constraint, the second objective constraint and the third objective constraint.

7. The method according to claim 1, characterized in that Construct the second constraint group, including: The power disaster recovery constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain the fourth objective constraint: in, and are the maximum capacity of up / down adjustment in response to electricity demand, and are binary indicator variables for up / down adjustment of electricity demand response, respectively; The thermal energy disaster recovery constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain the fifth objective constraint: in, and are the maximum capacity for up / down adjustment in response to thermal energy demand, and A binary indicator variable for up / down adjustment of heat demand response; A cooling capacity disaster recovery model of the virtual power plant corresponding to the power plant operation model is constructed to obtain the sixth objective constraint: in, and are the maximum capacity of up / down adjustment in response to cooling energy demand, and are binary indicator variables for up / down adjustment of cooling energy demand response, respectively; The fourth objective constraint, the fifth objective constraint and the sixth objective constraint are combined to obtain the second constraint group.

8. A scheduling device for a virtual power plant based on multi-energy demand response, characterized in that: The device comprises: A first acquisition unit is used to acquire historical operation data of the virtual power plant, wherein the historical operation data includes grid dispatching parameters and net income of the power plant, wherein the grid dispatching parameters include at least electric energy interaction data, thermal energy interaction data and cooling interaction data between the virtual power plant and the main grid, electric energy unit price, thermal energy unit price and cooling unit price, wind turbine unit cost, BMC unit cost and SMC unit cost; A first construction unit is used to construct a power plant operation model according to the historical operation data, wherein the power plant operation model is used to simulate the change trend of the power plant net income with the grid dispatching parameters; A second construction unit is used to construct a first constraint group and a second constraint group, wherein the first constraint group includes operating parameter constraints of the power plant operation model, the second constraint group includes disaster recovery constraints of the power plant operation model, the first constraint group includes at least power balance constraints, thermal energy balance constraints and cold power balance constraints of the virtual power plant, and the second constraint group includes at least power disaster recovery constraints, thermal energy disaster recovery constraints and cold capacity disaster recovery constraints; A calculation unit is used to solve the power plant operation model under the constraints of the first constraint group and the second constraint group, with the maximum net profit of the power plant as the objective function to obtain target scheduling parameters, and control the operation of the virtual power plant according to the target scheduling parameters.

9. 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 7.

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 7.

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