Energy storage power station coordinated optimization scheduling method and system suitable for virtual power plant

Abstract

The invention discloses an energy storage power station coordinated optimization scheduling method and system suitable for a virtual power plant, and the method comprises the steps: enabling the virtual power plant to serve as a whole to report the energy storage operation parameters of the virtual power plant, and receiving a scheduling instruction; the virtual power plant determines a target function and a constraint condition of lower-level optimization scheduling based on the scheduling instruction; the virtual electric field is solved based on the objective function and the constraint condition, the operation mode of the energy storage power stations in the virtual electric field is determined, the charge state of each energy storage power station is updated based on the operation mode, and coordinated optimization scheduling of the energy storage power stations is achieved. According to the method, optimal scheduling can be efficiently carried out on the energy storage power station, the loss cost of the energy storage power station can be reduced, and technical support can be provided for coordinated scheduling of the energy storage power station in the virtual power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization scheduling and operation, and more specifically, to a method and system for coordinated optimization scheduling of energy storage power stations applicable to virtual power plants. Background Art

[0002] With the rapid growth of new energy installed capacity, the adverse effects of the randomness and volatility of new energy power generation have gradually become significant, and have become one of the main problems for the further development of new energy power generation. Energy storage power stations have the characteristics of flexible time adjustment and wide spatial distribution, providing an effective solution for the consumption of new energy. However, due to the differences in parameters such as the scale, charging and discharging power, and capacity of energy storage power stations, their extensiveness and diversity lead to high marginal costs for energy storage power station transactions under unified dispatch, and it is difficult for the dispatching department to uniformly dispatch and manage the energy storage power stations in the system. Therefore, it is necessary to aggregate energy storage power stations into virtual power plants and participate in the management of dispatching departments and trading markets in the form of virtual power plants.

[0003] A virtual power plant is an organization that aggregates distributed power sources, loads, and energy storage power stations in a certain area and participates in grid dispatching and operation as a whole. From an external perspective, the energy storage in the virtual power plant is an energy storage aggregate that is controlled and managed by the virtual power plant. The aggregate participates in dispatching and electricity market as a whole, obtains the overall operation curve of energy storage, and then sends it to each energy storage power station.

[0004] Therefore, a coordinated optimization scheduling method for energy storage power stations suitable for virtual power plants is needed. Summary of the invention

[0005] The present invention proposes a coordinated optimization scheduling method and system for energy storage power stations applicable to virtual power plants, so as to solve the problem of how to optimize the scheduling of energy storage power stations.

[0006] In order to solve the above problem, according to one aspect of the present invention, a coordinated optimization scheduling method for energy storage power stations applicable to virtual power plants is provided, the method comprising:

[0007] The virtual power plant reports its own energy storage operating parameters as a whole and receives dispatch instructions;

[0008] The virtual power plant determines the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction;

[0009] The virtual electric field is solved based on the objective function and constraint conditions, the operation mode of the energy storage power station in the virtual electric field is determined, and the charge state of each energy storage power station is updated based on the operation mode to achieve coordinated optimization scheduling of the energy storage power stations.

[0010] Preferably, the operating parameters include: its own state of charge, maximum charging and discharging power, whether it can accept scheduling instructions or market trading instructions; the scheduling instructions include: the operating mode of the virtual power plant in the next scheduling period, and the operating mode includes: charging power or discharging power.

[0011] Preferably, the virtual power plant determines the maximum charging and discharging power in the following manner, including:

[0012]

[0013]

[0014] Among them, P ch is the maximum charging power reported by the virtual power plant; t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction; n is the number of energy storage power stations; is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant; E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc The maximum discharge power reported by the virtual power plant to the outside world; is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant; is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

[0015] Preferably, the virtual power plant determines the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction, including:

[0016] The lower-level optimal scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimal scheduling is:

[0017]

[0018]

[0019]

[0020] Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t; n is the number of energy storage power stations; C t is the total investment cost of the energy storage power station; D r Indicates the rated discharge depth of energy storage; N r D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth; a is the actual discharge depth of energy storage; N a The discharge depth D corresponding to the energy storage a The actual number of cycles when

[0021] The power balance constraint is determined as:

[0022]

[0023] Among them, P 总 (t) is the total energy storage power delivered at time t; and are the charging and discharging power of the i-th energy storage power station at time t respectively;

[0024] The operating power constraint is determined as:

[0025]

[0026] in, and are the rated minimum and maximum charging powers of the i-th energy storage power station respectively; and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively;

[0027] The energy storage constraint is determined as:

[0028] E min,i ≤E i (t)≤E max,i ,

[0029] Among them, E min,i and E max,i They represent the lower and upper limits of energy of the i-th energy storage station respectively; E i (t) represents the energy stored in the i-th energy storage power station at time t.

[0030] Preferably, the updating of the charge state of each energy storage power station based on the operation mode includes:

[0031]

[0032] Among them, E i (t+1) and E i (t) represents the energy stored in the i-th energy storage power station at time t+1 and time t respectively; and are the charging efficiency and discharging efficiency of energy storage station i respectively; and are the charging and discharging power of the i-th energy storage power station at time t respectively.

[0033] According to another aspect of the present invention, a coordinated optimization scheduling system for energy storage power stations applicable to virtual power plants is provided, the system comprising:

[0034] The operating parameter reporting unit is used for the virtual power plant to report its own energy storage operating parameters as a whole to the outside world and receive dispatch instructions;

[0035] An objective function and constraint condition determination unit, used for the virtual power plant to determine the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction;

[0036] The optimization scheduling unit is used to solve the virtual electric field based on the objective function and constraint conditions, determine the operation mode of the energy storage power station in the virtual electric field, and update the charge state of each energy storage power station based on the operation mode to achieve coordinated optimization scheduling of the energy storage power station.

[0037] Preferably, the operating parameters include: its own state of charge, maximum charging and discharging power, whether it can accept scheduling instructions or market trading instructions; the scheduling instructions include: the operating mode of the virtual power plant in the next scheduling period, and the operating mode includes: charging power or discharging power.

[0038] Preferably, the operating parameter reporting unit, the virtual power plant determines the maximum charge and discharge power in the following manner, including:

[0039]

[0040]

[0041] Among them, P ch is the maximum charging power reported by the virtual power plant; t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction; n is the number of energy storage power stations; is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant; E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc The maximum discharge power reported by the virtual power plant to the outside world; is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant; is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

[0042] Preferably, the objective function and constraint condition determination unit, wherein the virtual power plant determines the objective function and constraint condition of the lower-level optimization scheduling based on the scheduling instruction, includes:

[0043] The lower-level optimal scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimal scheduling is:

[0044]

[0045]

[0046]

[0047] Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t; n is the number of energy storage power stations; C t is the total investment cost of the energy storage power station; D r Indicates the rated discharge depth of energy storage; N r D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth; a is the actual discharge depth of energy storage; N a The discharge depth D corresponding to the energy storage a The actual number of cycles when

[0048] The power balance constraint is determined as:

[0049]

[0050] Among them, P 总 (t) is the total energy storage power delivered at time t; and are the charging and discharging power of the i-th energy storage power station at time t respectively;

[0051] The operating power constraint is determined as:

[0052]

[0053] in, and are the rated minimum and maximum charging powers of the i-th energy storage power station respectively; and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively;

[0054] The energy storage constraint is determined as:

[0055] E min,i ≤E i (t)≤E max,i ,

[0056] Among them, E min,i and E max,i They represent the lower and upper limits of energy of the i-th energy storage station respectively; E i (t) represents the energy stored in the i-th energy storage power station at time t.

[0057] Preferably, the optimization scheduling unit updates the state of charge of each energy storage power station based on the operation mode, including:

[0058]

[0059] Among them, E i (t+1) and E i (t) represents the energy stored in the i-th energy storage power station at time t+1 and time t respectively; and are the charging efficiency and discharging efficiency of energy storage station i respectively; and are the charging and discharging power of the i-th energy storage power station at time t respectively.

[0060] The present invention provides a method and system for coordinated optimization and scheduling of energy storage power stations applicable to virtual power plants, including: the virtual power plant reports its own energy storage operating parameters as a whole to the outside world and receives scheduling instructions; the virtual power plant determines the objective function and constraints of the lower-level optimization scheduling based on the scheduling instructions; the virtual electric field solves based on the objective function and constraints, determines the operating mode of the energy storage power station in the virtual electric field, and updates the charge state of each energy storage power station based on the operating mode, so as to realize coordinated optimization and scheduling of the energy storage power station. The method of the present invention can efficiently optimize and schedule the energy storage power station, and can reduce the loss cost of the energy storage power station, and can provide technical support for the coordinated scheduling of energy storage power stations in virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0062] Figure 1 It is a flowchart of a method 100 for coordinating and optimizing scheduling of energy storage power stations for virtual power plants according to an embodiment of the present invention;

[0063] Figure 2 A diagram of an energy storage power station aggregation model of a virtual power plant according to an embodiment of the present invention;

[0064] Figure 3 A comparison chart of operating costs according to an embodiment of the present invention;

[0065] Figure 4 It is an interactive schematic diagram of a two-level optimized day-ahead optimal dispatching system for an energy storage power station according to an embodiment of the present invention;

[0066] Figure 5 It is a structural schematic diagram of a coordinated optimization scheduling system 500 for energy storage power stations used in virtual power plants according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0068] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0069] Existing methods are often limited by the different parameters of each energy storage power station when optimizing the scheduling of energy storage power stations, making it difficult to perform overall coordinated scheduling. The present invention aims to propose an energy storage coordinated optimization scheduling method for a virtual power plant to overcome the shortcomings of the existing methods.

[0070] Figure 1 FIG. 1 is a flow chart of a method 100 for coordinating and optimizing scheduling of energy storage power stations for virtual power plants according to an embodiment of the present invention. Figure 1 As shown, the energy storage power station coordinated optimization scheduling method applicable to virtual power plants provided by the embodiment of the present invention can efficiently optimize the scheduling of energy storage power stations and reduce the loss cost of energy storage power stations. The energy storage power station coordinated optimization scheduling method 100 applicable to virtual power plants provided by the embodiment of the present invention starts from step 101. In step 101, the virtual power plant reports its own energy storage operating parameters as a whole to the outside world and receives scheduling instructions.

[0071] Preferably, the operating parameters include: its own state of charge, maximum charging and discharging power, whether it can accept scheduling instructions or market trading instructions; the scheduling instructions include: the operating mode of the virtual power plant in the next scheduling period, and the operating mode includes: charging power or discharging power.

[0072] Preferably, the virtual power plant determines the maximum charging and discharging power in the following manner, including:

[0073]

[0074]

[0075] Among them, P ch is the maximum charging power reported by the virtual power plant; t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction; n is the number of energy storage power stations; is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant; E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc The maximum discharge power reported by the virtual power plant to the outside world; is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant; is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

[0076] In the present invention, the energy storage power station model is as follows Figure 2 After the virtual power plant submits dispatch information to the dispatching agency or submits transaction information to the market, it executes the instruction after receiving the dispatching instruction from the dispatching agency or the transaction information from the market.

[0077] Specifically, the virtual power plant reports its own energy storage operating parameters as a whole, including its own state of charge, maximum charge and discharge power, whether it can accept dispatch instructions or market transaction instructions, etc., and waits for dispatch instructions / transaction instructions. The instructions include the operation mode of the virtual power plant in the next dispatch period, mainly the charge / discharge power.

[0078] Among them, the parameters reported by the virtual power plant to the outside are determined by the energy storage power stations it contains. Since the maximum charging and discharging power, maximum capacity and other parameters of the energy storage power stations are different, the overall charging and discharging power of the energy storage reported by the virtual power plant to the outside should be calculated according to the following formula:

[0079]

[0080]

[0081] Where t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction, P ch is the maximum charging power reported by the virtual power plant. is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant, E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc is the maximum discharge power reported by the virtual power plant to the outside world, is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant, is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

[0082] In step 102, the virtual power plant determines the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction.

[0083] Preferably, the virtual power plant determines the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction, including:

[0084] The lower-level optimal scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimal scheduling is:

[0085]

[0086]

[0087]

[0088] Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t; n is the number of energy storage power stations; C t is the total investment cost of the energy storage power station; D r Indicates the rated discharge depth of energy storage; N r D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth; a is the actual discharge depth of energy storage; N a The discharge depth D corresponding to the energy storage a The actual number of cycles when

[0089] The power balance constraint is determined as:

[0090]

[0091] Among them, P 总 (t) is the total energy storage power delivered at time t; and are the charging and discharging power of the i-th energy storage power station at time t respectively;

[0092] The operating power constraint is determined as:

[0093]

[0094] in, and are the rated minimum and maximum charging powers of the i-th energy storage power station respectively; and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively;

[0095] The energy storage constraint is determined as:

[0096] E min,i ≤E i (t)≤E max,i ,

[0097] Among them, E min,i and E max,i They represent the lower and upper limits of energy of the i-th energy storage station respectively; E i(t) represents the energy stored in the i-th energy storage power station at time t.

[0098] In the present invention, according to the instruction content, the virtual power plant coordinates and optimizes the energy storage curve. Due to the battery degradation phenomenon during the operation of energy storage, the degradation cost of energy storage must be taken into account when considering the operating cost and economic benefits of energy storage. The life loss cost of a single charge and discharge cycle of energy storage can be calculated according to formula (4) (5). Therefore, the lower-level optimization scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimization scheduling is:

[0099]

[0100]

[0101]

[0102] Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t, C t is the total investment cost of the energy storage power station, D r Indicates the rated discharge depth of energy storage, N r D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth, a is the actual discharge depth of the energy storage, N a The discharge depth D corresponding to the energy storage a The actual number of cycles.

[0103] (1) The power balance constraint is

[0104]

[0105] Among them, P 总 (t) is the total energy storage power delivered at time t, and are the charging and discharging power of the i-th energy storage power station at time t respectively.

[0106] (2) The operating power constraint is

[0107]

[0108] in, and are the rated minimum and maximum charging powers of the i-th energy storage station, and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively.

[0109] (3) Energy storage capacity constraints

[0110] E min,i ≤Ei (t)≤E max,i (8)

[0111] Among them, E min,i and E max,i They represent the lower and upper limits of energy for the i-th energy storage station, E i (t) represents the energy stored in the i-th energy storage power station at time t.

[0112] In step 103, the virtual electric field is solved based on the objective function and constraint conditions to determine the operation mode of the energy storage power station in the virtual electric field, and the charge state of each energy storage power station is updated based on the operation mode to achieve coordinated optimization scheduling of the energy storage power stations.

[0113] Preferably, the updating of the charge state of each energy storage power station based on the operation mode includes:

[0114]

[0115] Among them, E i (t+1) and E i (t) represents the energy stored in the i-th energy storage power station at time t+1 and time t respectively; and are the charging efficiency and discharging efficiency of energy storage station i respectively; and are the charging and discharging power of the i-th energy storage power station at time t respectively.

[0116] In the present invention, the operation mode of the energy storage power station in the virtual power plant can be obtained by solving the coordinated optimization scheduling method. For the next scheduling period, the charge state of each energy storage power station should be updated according to the following formula:

[0117]

[0118] in, and are the charging efficiency and discharging efficiency of energy storage station i respectively.

[0119] When the status of the energy storage power station is updated, the virtual power plant can continue to participate in dispatch instructions or market transactions in the next dispatch period.

[0120] Taking the consumption calculation of a provincial power grid as an example, the installed power capacity of the provincial power grid is 40.3 million kilowatts, of which thermal power installed capacity is 3.93 million kilowatts, hydropower installed capacity is 11.93 million kilowatts, wind power installed capacity is 8.43 million kilowatts, and photovoltaic power installed capacity is 16.01 million kilowatts. The local total annual electricity consumption is 742 trillion kilowatt-hours, the maximum load is 10.45 million kilowatts, and the province's energy storage installed capacity is 600,000 kilowatts. The energy storage power stations in the province are divided into 5 virtual power plants, and the overall energy storage dispatch instructions generated by the consumption calculation are optimized. Compared with the traditional method, the dispatch effect of the energy storage power station is as follows: Figure 3 Specifically, taking one year as an example, the comparison results are shown in Table 1. It can be seen that the method proposed by the present invention can reduce the loss cost of the energy storage power station by about 6.5% compared with the method of independent scheduling of the energy storage power station.

[0121] Table 1 Cost comparison table

[0122]

[0123] The method of the present invention can be combined with a new energy production simulation system to form a two-level optimized virtual power plant energy storage power station day-ahead optimization dispatching system. Figure 4 As shown in the figure, the upper optimization optimizes and calculates the power supply operation mode in the power system through the new energy production simulation software to maximize the consumption of new energy. The lower optimization carries out the coordinated optimization scheduling of the virtual power plant energy storage power station based on the operation mode calculated by the upper optimization, obtains the operation mode of the energy storage power station, and reduces the overall operation loss of the energy storage power station. The combined system runs on a personal computer (PC, Persona | Computer) device. After the new energy production simulation software completes the upper optimization, the result will be provided to the lower optimization model in the form of an input text. After the model completes the calculation, the calculation result will be returned to the production simulation software in the form of a result text for verification and forming the final result.

[0124] Figure 5 FIG. 5 is a schematic diagram of a coordinated optimization scheduling system 500 for an energy storage power station used in a virtual power plant according to an embodiment of the present invention. Figure 5 As shown, the energy storage power station coordination optimization scheduling system 500 applicable to a virtual power plant provided in an embodiment of the present invention includes: an operation parameter reporting unit 501, an objective function and constraint condition determination unit 502 and an optimization scheduling unit 503.

[0125] Preferably, the operating parameter reporting unit 501 is used for the virtual power plant to report its own energy storage operating parameters as a whole to the outside world and receive dispatch instructions.

[0126] Preferably, the operating parameters include: its own state of charge, maximum charging and discharging power, whether it can accept scheduling instructions or market trading instructions; the scheduling instructions include: the operating mode of the virtual power plant in the next scheduling period, and the operating mode includes: charging power or discharging power.

[0127] Preferably, in the operating parameter reporting unit 501, the virtual power plant determines the maximum charging and discharging power in the following manner, including:

[0128]

[0129]

[0130] Among them, P ch is the maximum charging power reported by the virtual power plant; t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction; n is the number of energy storage power stations; is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant; E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc The maximum discharge power reported by the virtual power plant to the outside world; is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant; is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

[0131] Preferably, the objective function and constraint condition determination unit 502 is used for the virtual power plant to determine the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction.

[0132] Preferably, the objective function and constraint condition determination unit 502, the virtual power plant determines the objective function and constraint condition of the lower-level optimization scheduling based on the scheduling instruction, including:

[0133] The lower-level optimal scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimal scheduling is:

[0134]

[0135]

[0136]

[0137] Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t; n is the number of energy storage power stations; C t is the total investment cost of the energy storage power station; D r Indicates the rated discharge depth of energy storage; Nr D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth; a is the actual discharge depth of energy storage; N a The discharge depth D corresponding to the energy storage a The actual number of cycles when

[0138] The power balance constraint is determined as:

[0139]

[0140] Among them, P 总 (t) is the total energy storage power delivered at time t; and are the charging and discharging power of the i-th energy storage power station at time t respectively;

[0141] The operating power constraint is determined as:

[0142]

[0143] in, and are the rated minimum and maximum charging powers of the i-th energy storage power station respectively; and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively;

[0144] The energy storage constraint is determined as:

[0145] E min,i ≤E i (t)≤E max,i ,

[0146] Among them, E min,i and E max,i They represent the lower and upper limits of energy of the i-th energy storage station respectively; E i (t) represents the energy stored in the i-th energy storage power station at time t.

[0147] Preferably, the optimization scheduling unit 503 is used to solve the virtual electric field based on the objective function and constraint conditions, determine the operation mode of the energy storage power station in the virtual electric field, and update the charge state of each energy storage power station based on the operation mode to achieve coordinated optimization scheduling of the energy storage power stations.

[0148] Preferably, the optimization scheduling unit 503 updates the state of charge of each energy storage power station based on the operation mode, including:

[0149]

[0150] Among them, E i (t+1) and Ei (t) represents the energy stored in the i-th energy storage power station at time t+1 and time t respectively; and are the charging efficiency and discharging efficiency of energy storage station i respectively; and are the charging and discharging power of the i-th energy storage power station at time t respectively.

[0151] The energy storage power station coordinated optimization scheduling system 500 for a virtual power plant according to an embodiment of the present invention corresponds to the energy storage power station coordinated optimization scheduling method 100 for a virtual power plant according to another embodiment of the present invention, which will not be described in detail here.

[0152] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0153] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of said means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

[0154] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0156] 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 including 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.

[0157] 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.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A coordinated optimization scheduling method for energy storage power stations applicable to virtual power plants, characterized in that: The method comprises: The virtual power plant reports its own energy storage operating parameters as a whole and receives dispatch instructions; The virtual power plant determines the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction; The virtual electric field is solved based on the objective function and constraint conditions, the operation mode of the energy storage power station in the virtual electric field is determined, and the charge state of each energy storage power station is updated based on the operation mode to achieve coordinated optimization scheduling of the energy storage power stations.

2. The method according to claim 1, characterized in that The operating parameters include: its own state of charge, maximum charging and discharging power, and whether it can accept scheduling instructions or market trading instructions; the scheduling instructions include: the operating mode of the virtual power plant in the next scheduling period, and the operating mode includes: charging power or discharging power.

3. The method according to claim 2, characterized in that The virtual power plant determines the maximum charging and discharging power using the following methods, including: Among them, P ch is the maximum charging power reported by the virtual power plant; t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction; n is the number of energy storage power stations; is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant; E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc The maximum discharge power reported by the virtual power plant to the outside world; is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant; is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

4. The method according to claim 1, characterized in that: The virtual power plant determines the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction, including: The lower-level optimal scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimal scheduling is: Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t; n is the number of energy storage power stations; C t is the total investment cost of the energy storage power station; D r Indicates the rated discharge depth of energy storage; N r D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth; a is the actual discharge depth of energy storage; N a The discharge depth D corresponding to the energy storage a The actual number of cycles when The power balance constraint is determined as: Among them, P 总 (t) is the total energy storage power delivered at time t; and are the charging and discharging power of the i-th energy storage power station at time t respectively; The operating power constraint is determined as: in, and are the rated minimum and maximum charging powers of the i-th energy storage power station respectively; and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively; The energy storage constraint is determined as: E min,i ≤E i (t)≤E max,i , Among them, E min,i and E max,i They represent the lower and upper limits of energy of the i-th energy storage station respectively; E i (t) represents the energy stored in the i-th energy storage power station at time t.

5. The method according to claim 1, characterized in that The updating of the charge state of each energy storage power station based on the operation mode includes: Among them, E i (t+1) and E i (t) represents the energy stored in the i-th energy storage power station at time t+1 and time t respectively; and are the charging efficiency and discharging efficiency of energy storage station i respectively; and are the charging and discharging power of the i-th energy storage power station at time t respectively.

6. A coordinated optimization dispatching system for energy storage power stations applicable to virtual power plants, characterized in that: The system comprises: The operating parameter reporting unit is used for the virtual power plant to report its own energy storage operating parameters as a whole to the outside world and receive dispatch instructions; An objective function and constraint condition determination unit, used for the virtual power plant to determine the objective function and constraint conditions of the lower-level optimization scheduling based on the scheduling instruction; The optimization scheduling unit is used to solve the virtual electric field based on the objective function and constraint conditions, determine the operation mode of the energy storage power station in the virtual electric field, and update the charge state of each energy storage power station based on the operation mode to achieve coordinated optimization scheduling of the energy storage power station.

7. The system according to claim 6, characterized in that The operating parameters include: its own state of charge, maximum charging and discharging power, and whether it can accept scheduling instructions or market trading instructions; the scheduling instructions include: the operating mode of the virtual power plant in the next scheduling period, and the operating mode includes: charging power or discharging power.

8. The system according to claim 7, characterized in that The operating parameter reporting unit, the virtual power plant determines the maximum charge and discharge power by the following method, including: Among them, P ch is the maximum charging power reported by the virtual power plant; t is the tth dispatch period in which the energy storage power station participates in dispatch or market transaction; n is the number of energy storage power stations; is the nominal maximum charging power of the i-th energy storage power station in the virtual power plant; E i (t) is the amount of electricity stored in the i-th energy storage power station in the virtual power plant; P dc The maximum discharge power reported by the virtual power plant to the outside world; is the nominal maximum discharge power of the i-th energy storage power station in the virtual power plant; is the nominal maximum storage capacity of the i-th energy storage power station in the virtual power plant.

9. The system according to claim 6, characterized in that The objective function and constraint condition determination unit, the virtual power plant determines the objective function and constraint condition of the lower-level optimization scheduling based on the scheduling instruction, including: The lower-level optimal scheduling aims to minimize the life loss cost of energy storage in the virtual power plant. The objective function of the lower-level optimal scheduling is: Among them, C loss,i (t) is the operating cost of the i-th energy storage power station in period t; n is the number of energy storage power stations; C t is the total investment cost of the energy storage power station; D r Indicates the rated discharge depth of energy storage; N r D is the rated charge and discharge cycle number of the energy storage at the rated discharge depth; a is the actual discharge depth of energy storage; N a The discharge depth D corresponding to the energy storage a The actual number of cycles when The power balance constraint is determined as: Among them, P 总 (t) is the total energy storage power delivered at time t; and are the charging and discharging power of the i-th energy storage power station at time t respectively; The operating power constraint is determined as: in, and are the rated minimum and maximum charging powers of the i-th energy storage power station respectively; and are the rated minimum and maximum discharge powers of the i-th energy storage power station respectively; The energy storage constraint is determined as: E min,i ≤E i (t)≤E max,i , Among them, E min,i and E max,i They represent the lower and upper limits of energy of the i-th energy storage station respectively; E i (t) represents the energy stored in the i-th energy storage power station at time t.

10. The system according to claim 6, characterized in that The optimization scheduling unit updates the charge state of each energy storage power station based on the operation mode, including: Among them, E i (t+1) and E i (t) represents the energy stored in the i-th energy storage power station at time t+1 and time t respectively; and are the charging efficiency and discharging efficiency of energy storage station i respectively; and are the charging and discharging power of the i-th energy storage power station at time t respectively.

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