Virtual Power Plant Operation Method and Device Based on Power Distribution System Constraints

Abstract

This invention discloses a method and apparatus for operating a virtual power plant based on distribution system constraints. The method includes: receiving a first distribution system constraint issued by a distribution system operator; acquiring historical distribution system constraints, constraint information of the distribution system, and power output prediction data of multiple distributed energy resources issued by the distribution system operator within a historical time period; processing the historical distribution system constraints, constraint information, and power output prediction data using a constraint prediction model to obtain a second distribution system constraint; adjusting an initial resource scheduling strategy based on the second distribution system constraint to obtain a first target resource scheduling strategy; and scheduling multiple distributed energy resources according to the first target resource scheduling strategy. This invention solves the technical problem in related technologies where the operation strategy of a virtual power plant depends on the distribution system constraints issued by the distribution system operator, resulting in limitations in formulating operation strategies.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant technology, and more specifically, to a method and apparatus for operating a virtual power plant based on power distribution system constraints. Background Technology

[0002] With the decarbonization, decentralization, and digitalization of power systems, the shift from centralized to distributed power systems has led to the widespread dissemination of distributed energy resources in distribution networks. Virtual power plants, as aggregates of distributed energy power traded in the electricity market using information and communication technologies, are attracting increasing attention. However, virtual power plants operating solely based on economic feasibility may cause distribution system stability issues, such as voltage violations and power congestion.

[0003] Existing virtual power plant (VPP) operating strategies heavily rely on distribution system constraint (DSC) information provided by distribution system operators (DSOs), which limits the flexibility and profitability of VPPs in the face of uncertainty. VPPs are typically in a passive state, relying on DSO information rather than proactively predicting the uncertainty of DSOs and formulating operating strategies accordingly.

[0004] Regarding the issue that the operation strategy of virtual power plants relies on the constraints of the distribution system issued by the distribution system operator, which restricts the formulation of operation strategies, no effective solution has yet been proposed. Summary of the Invention

[0005] This invention provides a method and apparatus for operating a virtual power plant based on distribution system constraints, in order to at least solve the technical problem in the related art that the operation strategy of a virtual power plant depends on the distribution system constraints issued by the distribution system operator, which makes the virtual power plant relatively limited in formulating operation strategies.

[0006] According to one aspect of the present invention, a method for operating a virtual power plant based on distribution system constraints is provided, comprising: receiving a first distribution system constraint issued by a distribution system operator, wherein the first distribution system constraint is a constraint condition calculated by the distribution system operator according to an initial resource scheduling strategy sent by the virtual power plant, the initial resource scheduling strategy being a strategy for scheduling multiple distributed energy resources, and the distribution system operator constraining the total output power of the multiple distributed energy resources to ensure that the power output value of the distribution system is within a predetermined range; acquiring historical distribution system constraints issued by the distribution system operator, constraint information of the distribution system, and power output prediction data of the multiple distributed energy resources within a historical time period; and processing the historical distribution system constraints, the constraint information, and the power output prediction data using a constraint prediction model to obtain a second distribution system constraint, wherein the constraint prediction model uses multiple training sets. The training data is obtained through machine learning. Each set of training data includes: sample input data and sample second power distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical power distribution system constraints, sample constraint information, and sample power output prediction data. The initial resource scheduling strategy is adjusted according to the second power distribution system constraints to obtain a first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that, under the condition that the power output value meets the predetermined range, makes the total output power exceed the first power constraint range but not exceed the second power constraint range. The first power constraint range is the range of the total output power constrained by the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained by the second power distribution system constraints. The multiple distributed energy resources are scheduled according to the first target resource scheduling strategy.

[0007] Optionally, before receiving the first distribution system constraint issued by the distribution system operator, the virtual power plant operation method based on the distribution system constraint further includes: evaluating the current status, available capacity, and expected power output data of the multiple distributed energy resources to obtain evaluation results; generating the initial resource scheduling strategy for scheduling the multiple distributed energy resources based on the evaluation results; and sending the initial resource scheduling strategy to the distribution system operator.

[0008] Optionally, before sending the initial resource scheduling strategy to the power distribution system operator, the virtual power plant operation method based on power distribution system constraints further includes: establishing a collaborative operation framework with the power distribution system operator to enable information interaction with the power distribution system operator through the collaborative operation framework.

[0009] Optionally, the virtual power plant operation method based on power distribution system constraints further includes: receiving the first power distribution system constraint issued by the power distribution system operator, and simultaneously receiving the penalty rules issued by the power distribution system operator, wherein the penalty rules are used to predict penalty fines when the total output power of the multiple distributed energy resources is not within the first power constraint range and the power output value is not within the predetermined range.

[0010] Optionally, the virtual power plant operation method based on distribution system constraints further includes: constructing an operation model, wherein the operation model is used to adjust the scheduling of multiple distributed energy resources; solving the operation model using an optimization algorithm to obtain the optimal solution of the operation model, wherein the optimal solution is the solution of the objective function in the operation model that maximizes the profit of the virtual power plant; adjusting the initial resource scheduling strategy according to the optimal solution under the conditions of satisfying the first distribution system constraints and the second distribution system constraints to obtain a second target resource scheduling strategy; and scheduling the multiple distributed energy resources according to the second target resource scheduling strategy.

[0011] Optionally, an operational model is constructed, including: obtaining current electricity sales revenue, loss costs, and penalties, wherein the electricity sales revenue is the difference between the revenue from supplying electricity to the distribution system and the expenditure from purchasing electricity from the distribution system; the loss costs are the costs incurred by multiple distributed energy resources; and the penalties are penalties predicted according to penalty rules under the condition that the total output power of the multiple distributed energy resources is not within a first power constraint range and the power output value is not within a predetermined range, wherein the penalty rules are rules issued by the distribution system operator; and establishing an objective function based on the electricity sales revenue, the loss costs, and the penalties, with the goal of maximizing profit, wherein the expression of the objective function is: t represents the running time label, T represents the total number of running times, w represents the running scenario label, W represents the total number of running scenarios, and π w C represents the probability of a scenario. sell，t C represents the revenue from the sale of electricity. ESS，w，t C represents the aforementioned loss cost. penalty，w，t The penalty is defined as follows: Constraints are applied to the output state, charging state, discharging state, shutdown state, and state of charge of the energy storage system during charging and discharging, resulting in multiple constraints. The energy storage system is one of multiple distributed energy resources. An operational model is constructed based on the objective function and the multiple constraints.

[0012] Optionally, obtaining the current electricity sales revenue, loss costs, and penalties includes: obtaining the exchange power with the distribution system, wherein the exchange power is the net output power value when multiple distributed energy resources trade electricity with the distribution system; if the net output power value is greater than zero, it indicates that the electricity is supplied to the distribution system; if the net output power value is less than zero, it indicates that the electricity is purchased from the distribution system; and calculating the electricity sales revenue based on the exchange power using a first formula, wherein the first formula is: C sell，t =R DA，t ×P VPP-DSO，t R DA，t P represents the electricity price of the previous operating segment in the current operating segment. VPP-DSO,t The exchange power is represented; the charging power and discharging power of the energy storage system are obtained; the loss cost is calculated using a second formula based on the charging power and the discharging power, wherein the second formula is: C ESS，w，t =(P ESS_ch，w，t +P ESS_dch，w，t C loss P ESS_ch，w，t P represents the charging power. ESS_dch，w，t C represents the discharge power. loss The wear and tear cost is represented; when the total output power is not within the first power constraint range and the power output value is not within the predetermined range, the difference between the total output power and the upper boundary of the first power constraint range is determined as the differential power; the penalty is calculated based on the differential power using a third formula, wherein the third formula is: C penalty，w，t =R penalty，t ×P exceed，w，t R penalty，t P represents the unit price of the penalty. exceed，w，t This represents the difference in power.

[0013] According to another aspect of the present invention, a virtual power plant operation device based on distribution system constraints is also provided, comprising: a first receiving unit, configured to receive a first distribution system constraint issued by a distribution system operator, wherein the first distribution system constraint is a constraint condition calculated by the distribution system operator according to an initial resource scheduling strategy sent by the virtual power plant, the initial resource scheduling strategy being a strategy for scheduling multiple distributed energy resources, and the distribution system operator constraining the total output power of the multiple distributed energy resources to ensure that the power output value of the distribution system is within a predetermined range; a first acquiring unit, configured to acquire historical distribution system constraints issued by the distribution system operator within a historical time period, constraint information of the distribution system, and power output prediction data of the multiple distributed energy resources; and a second acquiring unit, configured to process the historical distribution system constraints, the constraint information, and the power output prediction data using a constraint prediction model to obtain a second distribution system constraint of the virtual power plant, wherein the constraint prediction... The test model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second power distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical power distribution system constraints, sample constraint information, and sample power output prediction data. The third acquisition unit is used to adjust the initial resource scheduling strategy according to the second power distribution system constraints to obtain a first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that, under the condition that the power output value meets the predetermined range, makes the total output power exceed the first power constraint range but not exceed the second power constraint range. The first power constraint range is the range of the total output power constrained in the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained in the second power distribution system constraints. The first scheduling unit is used to schedule multiple distributed energy resources according to the first target resource scheduling strategy.

[0014] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: a fourth acquisition unit, configured to evaluate the current status, available capacity, and expected power output data of multiple distributed energy resources before receiving the first power distribution system constraints issued by the power distribution system operator, and obtain an evaluation result; a generation unit, configured to generate the initial resource scheduling strategy for scheduling the multiple distributed energy resources based on the evaluation result; and a sending unit, configured to send the initial resource scheduling strategy to the power distribution system operator.

[0015] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: an establishment unit, used to establish a collaborative operation framework with the power distribution system operator before sending the initial resource scheduling strategy to the power distribution system operator, so as to enable information interaction with the power distribution system operator through the collaborative operation framework.

[0016] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: a second receiving unit, configured to receive, while receiving the first power distribution system constraints issued by the power distribution system operator, a penalty rule issued by the power distribution system operator, wherein the penalty rule is used to predict penalty fines when the total output power of the multiple distributed energy resources is not within the first power constraint range and the power output value is not within the predetermined range.

[0017] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: a construction unit for constructing an operation model, wherein the operation model is used to adjust the scheduling of multiple distributed energy resources; a fifth acquisition unit for solving the operation model using an optimization algorithm to obtain the optimal solution of the operation model, wherein the optimal solution is the solution of the objective function in the operation model that maximizes the profit of the virtual power plant; a sixth acquisition unit for adjusting the initial resource scheduling strategy according to the optimal solution under the conditions of satisfying the first power distribution system constraints and the second power distribution system constraints to obtain a second target resource scheduling strategy; and a second scheduling unit for scheduling multiple distributed energy resources according to the second target resource scheduling strategy.

[0018] Optionally, the construction unit includes: a first acquisition module, used to acquire current electricity sales revenue, loss costs, and penalty fines, wherein the electricity sales revenue is the difference between the revenue from supplying electricity to the distribution system and the expenditure from purchasing electricity from the distribution system, the loss costs are the costs incurred by multiple distributed energy resources, and the penalty fines are the fines predicted according to penalty rules when the total output power of multiple distributed energy resources is not within a first power constraint range and the power output value is not within a predetermined range, and the penalty rules are rules issued by the distribution system operator; and an establishment module, used to establish an objective function based on the electricity sales revenue, the loss costs, and the penalty fines, with the goal of maximizing profit, wherein the expression of the objective function is: t represents the running time label, T represents the total number of running times, w represents the running scenario label, W represents the total number of running scenarios, and π w C represents the probability of a scenario. sell，t C represents the revenue from the sale of electricity.ESS，w，t C represents the aforementioned loss cost. penalty，w，t The first module represents the penalty fine; the second module is used to constrain the output state, charging state, discharging state, stopping state and charging state of the energy storage system during charging and discharging, respectively, to obtain multiple constraint conditions, wherein the energy storage system is one of the multiple distributed energy resources; the third module is used to construct an operating model based on the objective function and the multiple constraint conditions.

[0019] Optionally, the first acquisition module includes: a first acquisition submodule, configured to acquire the exchange power with the power distribution system, wherein the exchange power is the net output power value when multiple distributed energy resources engage in power trading with the power distribution system; if the net output power value is greater than zero, it indicates that the electrical energy is supplied to the power distribution system; if the net output power value is less than zero, it indicates that the electrical energy is purchased from the power distribution system; and a first calculation submodule, configured to calculate the electricity sales revenue based on the exchange power using a first formula, wherein the first formula is: C sell，t =R DA，t ×P VPP-DSO，t R DA，t P represents the electricity price of the previous operating segment in the current operating segment. VPP-DSO,t The second submodule represents the exchange power; the second acquisition submodule is used to acquire the charging power and discharging power of the energy storage system; the second calculation submodule is used to calculate the loss cost based on the charging power and the discharging power using a second formula, wherein the second formula is: C ESS，w，t =(P ESS_ch，w，t +P ES_dch，w，t C loss P ESS_ch，w，t P represents the charging power. ESS_dch，w，t C represents the discharge power. loss The system represents wear and tear costs; a determination submodule is used to determine the difference between the total output power and the upper boundary of the first power constraint range as the differential power when the total output power is not within the first power constraint range and the power output value is not within the predetermined range; a third calculation submodule is used to calculate the penalty fine based on the differential power using a third formula, wherein the third formula is: C penalty，w，t =R penalty，t ×P exceed，w，t R penalty，t P represents the unit price of the penalty. exceed，w，t This represents the difference in power.

[0020] According to another aspect of the present invention, a virtual power plant operation system based on power distribution system constraints is also provided, wherein the virtual power plant operation system based on power distribution system constraints uses any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0021] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0022] According to another aspect of the present invention, a processor is also provided, the processor being configured to run a program, wherein the program, when running, executes any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0023] According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, perform any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0024] In this embodiment of the invention, a first distribution system constraint is received from a distribution system operator. This first constraint is a constraint calculated by the distribution system operator based on an initial resource scheduling strategy sent by a virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources, used by the distribution system operator to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range. Historical distribution system constraints, constraint information of the distribution system, and power output prediction data of multiple distributed energy resources issued by the distribution system operator within a historical time period are acquired. A constraint prediction model is used to process the historical distribution system constraints, constraint information, and power output prediction data to obtain a second distribution system constraint. This constraint prediction model is developed using multiple sets of training data through machine learning. The training data is obtained by training the system. Each set of training data includes: sample input data and sample second distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical distribution system constraints, sample constraint information, and sample power output prediction data. The initial resource scheduling strategy is adjusted according to the second distribution system constraints to obtain the first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that ensures the total output power exceeds the first power constraint range but does not exceed the second power constraint range, while ensuring that the power output value meets the predetermined range. The first power constraint range is the range of the total output power constrained in the first distribution system constraints, and the second power constraint range is the range of the total output power constrained in the second distribution system constraints. Multiple distributed energy resources are scheduled according to the first target resource scheduling strategy. The above technical solutions achieve the goal of enabling virtual power plants to predict the constraints of the power distribution system independently, and flexibly adjust the scheduling of various distributed energy resources while maintaining the stability of the power distribution system. This achieves the technical effect of scheduling not only relying on the constraints issued by the power distribution system operator, but also allowing the virtual power plant to appropriately exceed the operating range limited by the constraints issued by the power distribution system operator while maintaining the stability of the power distribution system. This improves the flexibility of the virtual power plant in formulating operating strategies, and solves the technical problem in related technologies where the operation strategy of the virtual power plant depends on the constraints issued by the power distribution system operator, which restricts the virtual power plant in formulating operating strategies. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 This is a hardware structure block diagram of a mobile terminal for a virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention.

[0027] Figure 2 This is a flowchart of a virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention;

[0028] Figure 3 This is a flowchart of an optional virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention;

[0029] Figure 4 This is a flowchart of another optional virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of the collaborative operation mechanism processing flow according to an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of a virtual power plant operation device based on power distribution system constraints according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] As described in the background section, in related technologies, the operation strategy of virtual power plants relies on the distribution system constraints issued by the distribution system operator, which limits the formulation of operation strategies. To address these shortcomings, this invention provides a method and apparatus for operating a virtual power plant based on distribution system constraints.

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0036] The methods and embodiments provided in this invention can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a virtual power plant operation method based on power distribution system constraints, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0037] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the virtual power plant operation method based on power distribution system constraints in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station 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.

[0038] According to an embodiment of the present invention, a method embodiment of a virtual power plant operation method based on power distribution system constraints is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0039] Figure 2 This is a flowchart of a virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0040] Step S202: Receive the first distribution system constraint issued by the distribution system operator. The first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources. The distribution system operator uses this strategy to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range.

[0041] Optionally, the aforementioned distributed energy resources (DERs) may include, but are not limited to: photovoltaic systems, wind power systems, energy storage systems, small hydro power systems, fuel cells, microturbines, biomass power generation, and demand-side management (DSM), etc.

[0042] These distributed energy resources can be used individually or in combination to form a Virtual Power Plant (VPP). They are centrally managed and optimized through information and communication technologies to improve energy efficiency, reduce costs, and enhance grid stability and flexibility. In a virtual power plant, these resources can be dispatched according to market demand and grid operation conditions to maximize economic and environmental benefits.

[0043] In this embodiment, the virtual power plant can receive the first distribution system constraint issued by the distribution system operator and schedule each distributed energy resource according to the first distribution system constraint to ensure the stability of the distribution system.

[0044] It should be noted that the constraints imposed by the power distribution system operator on the virtual power plant are reflected in multiple aspects. In this embodiment, the constraint is mainly on the total output power of each distributed energy resource in the virtual power plant, that is, the electrical energy delivered by the virtual power plant to the power distribution system. The constraints on other aspects are similar in principle and will not be elaborated here. As for judging whether the power distribution system is stable, it can also be judged from multiple aspects. In this embodiment, the power output of the power distribution system can meet the user's needs, that is, whether the power output of the power distribution system can meet the preset requirements.

[0045] Step S204: Obtain historical distribution system constraints, distribution system constraint information, and power output forecast data of multiple distributed energy resources issued by the distribution system operator within the historical time period.

[0046] In this embodiment, the Distribution System Operator (DSO) primarily calculates the first distribution system constraints from the perspective of ensuring the stability and security of the distribution system, in order to ensure that the power grid operates within a safe range. Therefore, the distribution system is usually calculated in a conservative manner, which results in the virtual power plant always being in a passive state, relying on the first distribution system constraints issued by the DSO to formulate operating plans for each distributed energy resource. However, in actual operation, these constraints may change. Therefore, in order to better adapt to the uncertainty of changes in the first distribution system constraints, the virtual power plant can actively predict the constraints of the distribution system itself by obtaining historical distribution system constraints issued by the DSO within a historical time period, the constraint information of the distribution system, and the power output prediction data of multiple distributed energy resources. Based on the prediction results, the virtual power plant formulates corresponding operating strategies so that it can make fuller use of its resources without violating the DSO regulations, that is, while ensuring the stability of the distribution system.

[0047] Step S206: The constraint prediction model is used to process the historical distribution system constraints, constraint information and power output prediction data to obtain the second distribution system constraints. The constraint prediction model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical distribution system constraints, sample constraint information and sample power output prediction data.

[0048] Optionally, the aforementioned historical distribution system constraints refer to the constraint data issued by the distribution system operator to the virtual power plant within a historical time period.

[0049] Optionally, the aforementioned constraint information refers to the constraint information that the distribution system may face now and in the future, provided by the distribution system operator based on factors such as the current grid operation status, predicted load demand, and power generation capacity.

[0050] Optionally, the aforementioned power output forecast data refers to the forecast data of the output power of each distributed energy resource connected to the virtual power plant over a future period of time.

[0051] In this embodiment, the virtual power plant can use a constraint prediction model to process the acquired historical power distribution system constraints, constraint information, and power output prediction data to proactively predict the constraints of the power distribution system itself, thereby obtaining the second power distribution system constraints.

[0052] Step S208: Adjust the initial resource scheduling strategy according to the constraints of the second power distribution system to obtain the first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that, under the condition that the power output value meets the predetermined range, makes the total output power exceed the first power constraint range but not exceed the second power constraint range. The first power constraint range is the range of the total output power constrained in the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained in the second power distribution system constraints.

[0053] In this embodiment, since the first distribution system constraints issued by the distribution system operator have certain limitations for the virtual power plant, the virtual power plant may predict a more relaxed operating environment in some cases. Therefore, based on this, the virtual power plant can adjust its initial operating plan (i.e., initial resource scheduling strategy) according to the second distribution system constraints it predicts. This allows the virtual power plant to appropriately exceed the constraints limited by the first distribution constraints while ensuring the stability of the distribution system. This will not affect the stability of the distribution system and will also allow the virtual power plant to make fuller use of its resources, thus improving the flexibility of the virtual power plant in formulating operating strategies (strategies for scheduling various resources).

[0054] Of course, the virtual power plant no longer simply chooses to follow the first distribution system constraints issued by the distribution system operator, nor does it execute its operating strategy solely based on the second distribution system constraints it predicts. Instead, it comprehensively considers both the first distribution system constraints issued by the distribution system operator and the second distribution system constraints it predicts. When the second distribution system constraints it predicts are more lenient than the first distribution system constraints issued by the distribution system operator, it will not directly violate the DSO's constraints, but will try to find a balance between the two. This balance must ensure the stability of the distribution system while maximizing the economic benefits of the virtual power plant.

[0055] Step S210: Schedule multiple distributed energy resources according to the first target resource scheduling strategy.

[0056] In this embodiment, multiple distributed energy resources connected to the virtual power plant can be scheduled according to the first target resource scheduling strategy obtained by adjusting the initial resource scheduling strategy of the virtual power plant, so as to better adapt to market changes and grid demand.

[0057] As can be seen from the above steps, the technical solution provided by the above embodiments of the present invention can receive the first distribution system constraint issued by the distribution system operator. The first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources. The distribution system operator uses this strategy to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range. The system acquires historical distribution system constraints, distribution system constraint information, and power output prediction data of multiple distributed energy resources issued by the distribution system operator within a historical time period. It then processes the historical distribution system constraints, constraint information, and power output prediction data using a constraint prediction model to obtain the second distribution system constraint. The constraint prediction model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical distribution system constraints... The system incorporates system constraints, sample constraints, and sample power output prediction data. Based on the constraints of the second distribution system, the initial resource scheduling strategy is adjusted to obtain a first target resource scheduling strategy. This first target resource scheduling strategy aims to ensure that the total output power exceeds a first power constraint range but does not exceed a second power constraint range, while maintaining a predetermined range for power output. The first power constraint range is the range constrained by the first distribution system constraints, and the second power constraint range is the range constrained by the second distribution system constraints. By scheduling multiple distributed energy resources according to the first target resource scheduling strategy, the system achieves the goal of flexibly adjusting the scheduling of each distributed energy resource while maintaining the stability of the distribution system, based on the virtual power plant's self-prediction of distribution system constraints. This achieves the technical effect of scheduling not only based on the distribution system constraints issued by the distribution system operator, but also allows the virtual power plant to appropriately exceed the operating range limited by the constraints issued by the distribution system operator while maintaining the stability of the distribution system, thus improving the flexibility of the virtual power plant in formulating its operating strategy.

[0058] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that the operation strategy of virtual power plants depends on the distribution system constraints issued by the distribution system operator, which makes the virtual power plant relatively limited in formulating operation strategies.

[0059] The following is combined Figure 3 , Figure 4 and Figure 5 The embodiments of the present invention will be described in detail below. Figure 3 This is a flowchart of an optional virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention; Figure 4 This is a flowchart of another optional virtual power plant operation method based on power distribution system constraints according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the collaborative operation mechanism processing flow according to an embodiment of the present invention.

[0060] According to the above embodiments of the present invention, before receiving the first distribution system constraint issued by the distribution system operator, the virtual power plant operation method based on the distribution system constraint further includes: evaluating the current status, available capacity and expected power output data of multiple distributed energy resources to obtain evaluation results; generating an initial resource scheduling strategy for scheduling multiple distributed energy resources based on the evaluation results; and sending the initial resource scheduling strategy to the distribution system operator.

[0061] As above Figure 3 As shown, a virtual power plant first assesses various distributed energy resources to formulate an initial operational plan (i.e., an initial resource dispatch strategy). Specifically, the virtual power plant needs to conduct a comprehensive assessment of the distributed energy resources it manages (such as solar photovoltaic, wind power, and energy storage systems) to understand their current status, available capacity, and expected output. Then, it analyzes electricity market demand and price fluctuations, predicting electricity demand and market prices over different time periods to determine the optimal timing and price for electricity sales. Next, it uses historical data, meteorological information, load forecasting models, and other tools to predict the output of distributed energy resources and establish mathematical models of electricity demand and supply. Based on the resource assessment, market analysis, and forecasting results, the virtual power plant formulates an initial operational plan. This plan needs to maximize economic benefits while ensuring the supply and demand balance of the electricity market, and is adjusted in accordance with relevant industry rules and standards. Finally, the preliminary operational plan is sent to the distribution system operator (DSO).

[0062] In an optional embodiment of the present invention, before sending the initial resource scheduling strategy to the power distribution system operator, the virtual power plant operation method based on power distribution system constraints further includes: establishing a collaborative operation framework with the power distribution system operator so as to enable information interaction with the power distribution system operator through the collaborative operation framework.

[0063] As above Figure 3 and Figure 4 As shown, a collaborative operation mechanism that considers the constraints of the power distribution system can be constructed. Within the collaborative operation framework between the power distribution system operator and the virtual power plant, after receiving the initial resource scheduling strategy sent by the virtual power plant, the power distribution system operator calculates the first power distribution system constraints based on the received initial resource scheduling strategy and sends them to the virtual power plant so that the virtual power plant can schedule its resources according to these constraints. The goal is to maximize profits while ensuring the stability of the power distribution system.

[0064] Regarding the interaction between power distribution system operators and virtual power plants, Figure 5 The specific interaction process is shown in the diagram.

[0065] In a specific embodiment of the present invention, the virtual power plant operation method based on power distribution system constraints further includes: receiving a first power distribution system constraint issued by a power distribution system operator, and simultaneously receiving a penalty rule issued by the power distribution system operator, wherein the penalty rule is used to predict a penalty when the total output power of multiple distributed energy resources is not within the first power constraint range and the power output value is not within a predetermined range.

[0066] As above Figure 4 and above Figure 5As shown, when issuing the first distribution system constraint to the virtual power plant, the distribution system operator also simultaneously sends penalty rules. The first distribution system constraint mainly restricts the operation of the virtual power plant to ensure the stability of the distribution system. Here, we take the total output power of multiple distributed energy resources in the virtual power plant as an example: Assume that the constraint range for the total output power of the virtual power plant in the first distribution system constraint is the first power constraint range. If the virtual power plant fails to comply with this first distribution system constraint, that is, if the total output power of the virtual power plant exceeds the first power constraint range, it may lead to the distribution system having to perform additional load reduction or generation adjustment. This is what the distribution system operator will punish in the virtual power plant. Power plants face economic penalties; conversely, if a virtual power plant complies with the first distribution system constraints, the distribution system operator may offer economic rewards to encourage such compliance. This demonstrates the dual role of rewards and penalties in ensuring that distribution system constraints are met. The distribution system's issuance of these penalty rules to virtual power plants allows them to predict potential penalties when adjusting to the second distribution system constraints. This means that while virtual power plants can flexibly adjust their operating strategies to maximize profits, they also need to consider the importance of complying with the first distribution system constraints, as this directly relates to their economic returns and the safe operation of the system.

[0067] The key to this cooperation mechanism lies in information sharing and coordination between virtual power plants and distribution system operators, aiming to balance economic benefits and system stability. While pursuing maximum profits, virtual power plants must also consider the importance of complying with distribution system constraints. Through this two-way incentive and constraint mechanism, virtual power plants and distribution system operators can jointly promote a more efficient and reliable power distribution system.

[0068] It should be noted that the penalty mechanism between virtual power plants and distribution system operators is that when the power output of a virtual power plant exceeds the constraints of the distribution system, it may lead to instability in the distribution system. Therefore, the virtual power plant will face economic penalties from the distribution system operator. However, if no instability issues occur in the distribution system, even if the virtual power plant outputs power exceeding the demand, the virtual power plant will not suffer economic losses.

[0069] Furthermore, if the total output power of the virtual power plant exceeds the first power constraint range (i.e., the output power value is greater than the appropriate power value required to maintain the stability of the power distribution system), this may lead to voltage violations or power congestion in the power distribution system, thereby affecting the system's stability. Conversely, if the total output power of the virtual power plant does not meet the first power constraint range (i.e., the output power value is less than the appropriate power value required to maintain the stability of the power distribution system), this may lead to an inability to meet system demands, which will also affect the system's stability. Therefore, in either case, the virtual power plant may be subject to economic penalties. In the embodiments of this invention, the economic penalty in the case where the total output power of the virtual power plant exceeds the first power constraint range is mainly described as an example. The other case is similar and will not be elaborated further.

[0070] In a preferred embodiment of the present invention, the virtual power plant operation method based on power distribution system constraints further includes: constructing an operation model, wherein the operation model is used to adjust the scheduling of multiple distributed energy resources; solving the operation model using an optimization algorithm to obtain the optimal solution of the operation model, wherein the optimal solution is the solution of the objective function in the operation model that maximizes the profit of the virtual power plant; adjusting the initial resource scheduling strategy according to the optimal solution under the conditions of satisfying the first power distribution system constraints and the second power distribution system constraints to obtain a second target resource scheduling strategy; and scheduling the multiple distributed energy resources according to the second target resource scheduling strategy.

[0071] As above Figure 4 As shown, in order to maximize profits while ensuring the stability of the power distribution system, a virtual power plant can be constructed with an operating model. This operating model is used to improve the economic benefits of the virtual power plant while ensuring the stability of the power distribution system. The operating model is a model obtained by establishing an objective function with the goal of maximizing the profits of the virtual power plant, taking into account the constraints of the energy storage system.

[0072] In a specific embodiment of the present invention, constructing an operating model includes: obtaining current electricity sales revenue, loss costs, and penalties, wherein electricity sales revenue is the difference between the revenue from supplying electricity to the distribution system and the expenditure on purchasing electricity from the distribution system; loss costs are the costs incurred by multiple distributed energy resources; and penalties are penalties predicted according to penalty rules when the total output power of multiple distributed energy resources is not within a first power constraint range and the power output value is not within a predetermined range, and the penalty rules are rules issued by the distribution system operator; based on electricity sales revenue, loss costs, and penalties, an objective function is established with profit maximization as the goal, wherein the expression of the objective function is: t represents the runtime index, T represents the total runtime, w represents the runtime scenario index, W represents the total runtime scenario, and π represents the total runtime scenario. w C represents the probability of a scenario. sell，t C represents revenue from electricity sales. ESS，w，t C represents the cost of loss. penalty，w，t The penalty is indicated by fines; constraints are imposed on the output state, charging state, discharging state, shutdown state, and charged state of the energy storage system during charging and discharging, resulting in multiple constraints. The energy storage system is one of multiple distributed energy resources; an operational model is constructed based on the objective function and multiple constraints.

[0073] In the above embodiments of the present invention, obtaining the current electricity sales revenue, loss costs, and penalties includes: obtaining the exchange power with the distribution system, wherein the exchange power is the net output power value when multiple distributed energy resources conduct electricity transactions with the distribution system; when the net output power value is greater than zero, it indicates that electricity is being supplied to the distribution system; when the net output power value is less than zero, it indicates that electricity is being purchased from the distribution system; and calculating the electricity sales revenue based on the exchange power using a first formula, wherein the first formula is: C sell，t =R DA，t ×P VPP-DSO，t R DA，t P represents the electricity price of the previous operating segment in the current operating segment. VPP-DSO，t Represents the exchange power; obtains the charging and discharging power of the energy storage system; calculates the loss cost based on the charging and discharging power using the second formula, where the second formula is: C ESS，w，t =(P ESS_ch，w，t +P ESS_dch，w，t C loss P ESS_ch，w，t P represents the charging power. ESS_dch，w，t C represents the discharge power. loss This represents wear and tear costs; when the total output power is not within the first power constraint range and the power output value is not within the predetermined range, the difference between the total output power and the upper boundary of the first power constraint range is determined as the differential power; based on the differential power, the penalty is calculated using the third formula, where the third formula is: C penalty，w，t =R penalty，t ×P exceed，w，t R penalty，t P represents the unit price of the penalty. exceed，w，t This indicates the differential power.

[0074] Specifically, a profit-maximizing operation model for a virtual power plant with a photovoltaic power generation system and an energy storage system can be constructed. An objective function is established with the goal of maximizing the profit of the virtual power plant. The expression of the objective function is shown in formula (1). This objective function is derived from the electricity sales revenue C.sell，t The cost of energy storage system operation (C) ESS，w，t And the expected penalties and fines for cooperative operation C penalty，w，t The calculation shows that the specific formula can be expressed as: C sell，t =R DA，t ×P VPP-DSO，t (2) P VPP-DSO,t =P PV，t +P ESS_dchw，t -P ESS_ch，w，t (3) C ESS，w，t =(P ESS_ch，w，t +P ESS_dch，w，t C loss (4) C penalty，w，t =R penalty，t ×P exceed，w，t (6), Where T represents the total running time, w represents the label of the running scenario, W represents the total number of running scenarios, and π w C represents the probability of a scenario. sell，t This represents the revenue from electricity sales, calculated as shown in formula (2), P VPP-DSO，t This represents the exchange power delivered by the virtual power plant to the distribution system. A positive value indicates electricity sales, while a negative value indicates electricity purchases. P VPP-DSO，t It can be calculated from the photovoltaic power generation and the discharge and charging power of the energy storage system, as shown in formula (3). PV，t P represents the photovoltaic power generation capacity. ESS_dch，w，t P represents the discharge power of the energy storage system. ESS_ch，w，t C represents the charging power of the energy storage system. ESS，w，t The loss cost is calculated based on the number of charge-discharge cycles of the energy storage system, as shown in formula (4), C loss The wear and tear cost of the energy storage system can be calculated using formula (5), C. ESS This indicates the unit price of the energy storage system. The term ESS indicates the lifecycle of an energy storage system. DOD The formula (6) represents the depth of discharge of the energy storage system, and the formula (6) represents the penalty C of the expected penalty. penalty，w，t It is represented by the amount of electricity generated exceeding the rated output when a penalty is imposed, P. exceed，w，t R is calculated from (i.e., differential power). penalty，t This indicates the penalty unit price.

[0075] The constraints of the energy storage system consist of formulas (7) to (11), where the output constraints during charging and discharging of the energy storage system can be expressed by formulas (7) and (8) respectively; formula (9) represents the state constraints of the energy storage system, determining the charging, discharging, and stopping states; formula (10) represents the state of charge (SOC); and formula (11) represents the constraints for the termination of the energy storage system, which can be specifically expressed as follows: I ESS_dch，w，t P min ≤P ESS_dch，w，t ≤I ESS_dch，w，t P max (7) I ESS_ch，w，t P min ≤P ESS_ch，w，t ≤I ESS_ch，w，t P max (8) I ESS_ch，w，t +I ESS_dch，w，t ≤1 (9) SOC min，w ≤SOC init，w +SOC w，t ≤SOC max，w (11), Among them, P min P represents the minimum value transmitted by the energy storage system. max I represents the maximum value transmitted by the energy storage system. ESS_ch，w，t and I ESS_dch，w，t State of the energy storage system is represented in binary, SOC. w，t State of charge (SOC) represents the state of charge of an energy storage system. init，w η represents the initial state of charge of the energy storage system. ch η represents the charging efficiency of an energy storage system. dch State of Charge (SOC) represents the discharge efficiency of an energy storage system. min，w State of charge (SOC) represents the minimum state of charge of an energy storage system. max，w This indicates the maximum state of charge of the energy storage system.

[0076] In addition, as mentioned above Figure 4 As shown, constraints for calculating the unexpected power output of the virtual power plant also need to be constructed. The virtual power plant determines P by calculating electricity sales revenue and penalties. VPP-DSO，t P exceed，w，tThe excess output power when the power distribution system has a problem is represented by the formulas (12)-(15). If the power sales of the virtual power plant are more than the first power distribution system constraint issued by the power distribution system operator and there is no problem with the power distribution system, then the power distribution system operator does not need to impose any penalty on the virtual power plant. However, if there is any problem with the power distribution system and the power distribution system operator controls some resources in the power distribution system, then the virtual power plant should be responsible for the excess power it provides.

[0077] The virtual power plant uses its own predicted distribution system constraints and the distribution system constraints provided by the distribution system operator to calculate P. exceed，w，t Since the actual distribution system constraints are unknown, the expected penalty is calculated using the distribution system constraints predicted by the virtual power plant. The purpose of formula (12) is to ensure that P exceed，w，t The value of P will not exceed the maximum possible value of the excess power jointly determined by the second distribution system constraints predicted by the virtual power plant and the first distribution system constraints provided by the distribution system operator, that is, ensuring that the excess power calculated by the virtual power plant does not exceed the actual excess power that may occur under any given circumstances; when the distribution system constraints predicted by the virtual power plant are greater than the distribution system constraints provided by the distribution system operator, P exceed，w，t The calculation is performed on the power exceeding the distribution system constraints predicted by the virtual power plant, using formula (13) for constraint; when the distribution system constraints provided by the distribution system operator are greater than the distribution system constraints predicted by the virtual power plant, P exceed，w，t The calculation is performed on the power exceeding the power distribution system constraints provided by the power distribution system operator, and the constraint is applied using formula (14). In addition, since the power exceeding the acceptable power of the power distribution system cannot be negative, formula (15) imposes relevant constraints on this.

[0078] The above formulas (12)-(15) can be specifically expressed as follows: DSC VPP，w，t -DSC DSO，t ≤MI PC，w，t (13) DSC DSO，t _DSC VPP，w，t ≤M(1-I PC，w，t (14) 0≤P exceed，w，t (15), Among them, DSC VPP，w，t DSC represents the distribution system constraints predicted by the virtual power plant itself. DSO，t Indicates the power distribution system constraints provided by the power distribution system operator; I PC，w，tThe state variable indicates whether the distribution system constraints predicted by the virtual power plant itself exceed the distribution system constraints provided by the distribution system operation. If it exceeds, it is 1; otherwise, it is 0. M represents a sufficiently large constant.

[0079] The second distribution system constraints predicted by the virtual power plant can be expressed by formulas (16) and (17), in the form of a distribution. Formula (16) indicates that the distribution system constraints predicted by the virtual power plant itself follow a normal distribution, as shown below: Where, μ t This represents the mean. Represents standard deviation, when The smaller the value, the better the virtual power plant predicts the constraints of the actual power distribution system.

[0080] As above Figure 3 As shown, after the virtual power plant adjusts the operation plans of each distributed energy resource according to the first distribution system constraints issued by the distribution system operator and the self-predicted Hill distribution system constraints, the virtual power plant dispatches the distributed energy resources according to the adjusted operation plan. The distribution system operator manages the distribution system independently. The virtual power plant sends the operation results to the distribution system operator. When a problem occurs in the distribution system, the distribution system operator checks whether the virtual power plant violates the first distribution system constraints. If the distribution system has a problem for any reason, including the virtual power plant over-generating electricity, the distribution system operator will resolve the problem and calculate a penalty based on the virtual power plant's operation results. The distribution system operator imposes a fine on the virtual power plant. After the operation is completed, the distribution system operator will publish the virtual power plant's operation results and the penalty settlement history to improve the transparency of the distribution system operation based on distribution system constraints.

[0081] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0083] According to embodiments of the present invention, a virtual power plant operation apparatus based on distribution system constraints is also provided for implementing the above-described virtual power plant operation method based on distribution system constraints. Figure 6 This is a schematic diagram of a virtual power plant operation device based on power distribution system constraints according to an embodiment of the present invention, as shown below. Figure 6 As shown, the device includes: a first receiving unit 61, a first acquiring unit 63, a second acquiring unit 65, a third acquiring unit 67, and a first scheduling unit 69. The following is a detailed description of this virtual power plant operation device based on power distribution system constraints.

[0084] The first receiving unit 61 is used to receive the first distribution system constraint issued by the distribution system operator. The first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources. The distribution system operator uses this strategy to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range.

[0085] The first acquisition unit 63 is used to acquire historical power distribution system constraints, power distribution system constraint information, and power output prediction data of multiple distributed energy resources issued by the power distribution system operator within a historical time period.

[0086] The second acquisition unit 65 is used to process historical power distribution system constraints, constraint information and power output prediction data using a constraint prediction model to obtain the second power distribution system constraints of the virtual power plant. The constraint prediction model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second power distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical power distribution system constraints, sample constraint information and sample power output prediction data.

[0087] The third acquisition unit 67 is used to adjust the initial resource scheduling strategy according to the second power distribution system constraints to obtain a first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that, under the condition that the power output value meets a predetermined range, makes the total output power exceed the first power constraint range but not exceed the second power constraint range. The first power constraint range is the range of the total output power constrained in the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained in the second power distribution system constraints.

[0088] The first scheduling unit 69 is used to schedule multiple distributed energy resources according to the first target resource scheduling strategy.

[0089] It should be noted that the first receiving unit 61, the first acquiring unit 63, the second acquiring unit 65, the third acquiring unit 67 and the first scheduling unit 69 mentioned above correspond to steps S202 to S210 in the above embodiments. The five units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments.

[0090] As can be seen from the above, in the scheme described in the above embodiments of the present invention, a first receiving unit can be used to receive a first distribution system constraint issued by a distribution system operator. The first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources, used by the distribution system operator to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range. Then, a first acquiring unit is used to acquire historical distribution system constraints, distribution system constraint information, and power output prediction data of multiple distributed energy resources issued by the distribution system operator within a historical time period. Next, a second acquiring unit uses a constraint prediction model to process the historical distribution system constraints, constraint information, and power output prediction data to obtain the second distribution system constraint of the virtual power plant. The constraint prediction model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second distribution system constraints corresponding to the sample input data. The sample input data includes: historical samples... The system first obtains power distribution system constraints, sample constraints, and sample power output prediction data. Then, a third acquisition unit adjusts the initial resource scheduling strategy based on the second power distribution system constraints to obtain a first target resource scheduling strategy. This first target resource scheduling strategy aims to ensure that the total output power exceeds a first power constraint range but does not exceed a second power constraint range, while maintaining a predetermined power output value. The first power constraint range is the range constrained by the first power distribution system constraints, and the second power constraint range is the range constrained by the second power distribution system constraints. Finally, a first scheduling unit schedules multiple distributed energy resources according to the first target resource scheduling strategy. This achieves the goal of flexibly adjusting the scheduling of distributed energy resources while maintaining the stability of the power distribution system, based on the virtual power plant's self-prediction of power distribution system constraints. This realizes the technical effect of scheduling not only based on the power distribution system constraints issued by the power distribution system operator, but also allows the virtual power plant to appropriately exceed the operating range limited by the constraints issued by the power distribution system operator while maintaining the stability of the power distribution system, thus improving the flexibility of the virtual power plant in formulating its operating strategy.

[0091] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that the operation strategy of virtual power plants depends on the distribution system constraints issued by the distribution system operator, which makes the virtual power plant relatively limited in formulating operation strategies.

[0092] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: a fourth acquisition unit, used to evaluate the current status, available capacity and expected power output data of multiple distributed energy resources before receiving the first power distribution system constraints issued by the power distribution system operator, and obtain the evaluation results; a generation unit, used to generate an initial resource scheduling strategy for scheduling multiple distributed energy resources based on the evaluation results; and a sending unit, used to send the initial resource scheduling strategy to the power distribution system operator.

[0093] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: an establishment unit, used to establish a collaborative operation framework with the power distribution system operator before sending the initial resource scheduling strategy to the power distribution system operator, so as to enable information interaction with the power distribution system operator through the collaborative operation framework.

[0094] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: a second receiving unit, used to receive, while receiving the first power distribution system constraints issued by the power distribution system operator, penalty rules issued by the power distribution system operator, wherein the penalty rules are used to predict penalty fines when the total output power of multiple distributed energy resources is not within the first power constraint range and the power output value is not within a predetermined range.

[0095] Optionally, the virtual power plant operation device based on power distribution system constraints further includes: a construction unit for constructing an operation model, wherein the operation model is used to adjust the scheduling of multiple distributed energy resources; a fifth acquisition unit for solving the operation model using an optimization algorithm to obtain the optimal solution of the operation model, wherein the optimal solution is the solution of the objective function in the operation model that maximizes the profit of the virtual power plant; a sixth acquisition unit for adjusting the initial resource scheduling strategy according to the optimal solution under the conditions of satisfying the first power distribution system constraints and the second power distribution system constraints to obtain a second objective resource scheduling strategy; and a second scheduling unit for scheduling multiple distributed energy resources according to the second objective resource scheduling strategy.

[0096] Optionally, the construction unit includes: a first acquisition module, used to acquire current electricity sales revenue, loss costs, and penalties, wherein the electricity sales revenue is the difference between the revenue from transmitting electricity to the distribution system and the expenditure on purchasing electricity from the distribution system; the loss costs are the costs incurred by multiple distributed energy resources; and the penalties are the penalties predicted according to penalty rules when the total output power of multiple distributed energy resources is not within a first power constraint range and the power output value is not within a predetermined range, and the penalty rules are rules issued by the distribution system operator; and a construction module, used to establish an objective function based on the electricity sales revenue, loss costs, and penalties, with the goal of maximizing profit, wherein the expression of the objective function is: t represents the running time label, T represents the total running time, w represents the running scenario label, W represents the total running scenario, and π represents the total number of running scenarios. w C represents the probability of a scenario. sell，t C represents revenue from electricity sales. ESS，w，t C represents the cost of loss. penalty，w，t The first module represents the penalty; the second module is used to constrain the output state, charging state, discharging state, stopping state and charging state of the energy storage system during charging and discharging, respectively, to obtain multiple constraints, wherein the energy storage system is one of multiple distributed energy resources; the third module is used to construct an operating model based on the objective function and multiple constraints.

[0097] Optionally, the first acquisition module includes: a first acquisition submodule, used to acquire the exchange power with the power distribution system, wherein the exchange power is the net output power value when multiple distributed energy resources trade electricity with the power distribution system; if the net output power value is greater than zero, it indicates that electrical energy is being supplied to the power distribution system; if the net output power value is less than zero, it indicates that electrical energy is being purchased from the power distribution system; and a first calculation submodule, used to calculate the electricity sales revenue based on the exchange power using a first formula, wherein the first formula is: C sell，t =R DA，t ×P VPP-DSO，t R DA，t P represents the electricity price of the previous operating segment in the current operating segment. VPP-DSO，t The first module represents the exchange power; the second acquisition submodule is used to acquire the charging power and discharging power of the energy storage system; the second calculation submodule is used to calculate the loss cost based on the charging power and discharging power using a second formula, wherein the second formula is: C ESS，w，t =(P ESS_ch，w，t +P ESS_dch，w，t C loss P ESS_ch，w，t P represents the charging power. ESS_dch，w，t C represents the discharge power. loss The first module represents wear and tear costs; the second module determines the difference between the total output power and the upper boundary of the first power constraint range as the differential power when the total output power is not within the first power constraint range and the power output value is not within the predetermined range; the third module calculates the penalty based on the differential power using a third formula, where the third formula is: C penalty，w，t =R penalty，t ×P exceed，w，t R penalty，t P represents the unit price of the penalty. exceed，w，t This indicates the differential power.

[0098] According to another aspect of the present invention, a virtual power plant operation system based on power distribution system constraints is also provided, wherein the virtual power plant operation system based on power distribution system constraints uses any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0099] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0100] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any communication device in a group of communication devices.

[0101] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: receiving a first distribution system constraint issued by a distribution system operator, wherein the first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant, the initial resource scheduling strategy being a strategy for scheduling multiple distributed energy resources, used by the distribution system operator to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range; acquiring historical distribution system constraints, distribution system constraint information, and power output prediction data of multiple distributed energy resources issued by the distribution system operator within a historical time period; processing the historical distribution system constraints, constraint information, and power output prediction data using a constraint prediction model to obtain a second distribution system constraint, wherein the constraint prediction model is used to make... The system is trained using machine learning with multiple sets of training data. Each set of training data includes: sample input data and sample second power distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical power distribution system constraints, sample constraint information, and sample power output prediction data. The initial resource scheduling strategy is adjusted according to the second power distribution system constraints to obtain the first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that ensures the total output power exceeds the first power constraint range but does not exceed the second power constraint range, while ensuring the power output value meets a predetermined range. The first power constraint range is the range of the total output power constrained by the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained by the second power distribution system constraints. Multiple distributed energy resources are scheduled according to the first target resource scheduling strategy.

[0102] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: evaluating the current status, available capacity, and expected power output data of multiple distributed energy resources to obtain evaluation results; generating an initial resource scheduling strategy for scheduling the multiple distributed energy resources based on the evaluation results; and sending the initial resource scheduling strategy to the power distribution system operator.

[0103] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: establishing a collaborative operation framework with the power distribution system operator to enable information exchange with the power distribution system operator through the collaborative operation framework.

[0104] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: receiving a first power distribution system constraint issued by the power distribution system operator, and simultaneously receiving a penalty rule issued by the power distribution system operator, wherein the penalty rule is used to predict a penalty when the total output power of multiple distributed energy resources is not within the first power constraint range and the power output value is not within a predetermined range.

[0105] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: constructing an operating model, wherein the operating model is used to adjust the scheduling of multiple distributed energy resources; solving the operating model using an optimization algorithm to obtain the optimal solution of the operating model, wherein the optimal solution is the solution of the objective function in the operating model that maximizes the profit of the virtual power plant; adjusting the initial resource scheduling strategy according to the optimal solution under the conditions of satisfying the first and second power distribution system constraints to obtain a second target resource scheduling strategy; and scheduling the multiple distributed energy resources according to the second target resource scheduling strategy.

[0106] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining current electricity sales revenue, loss costs, and penalty fines, wherein the electricity sales revenue is the difference between the revenue from supplying electricity to the distribution system and the expenditure on purchasing electricity from the distribution system, the loss costs are the costs incurred by multiple distributed energy resources, and the penalty fines are the fines predicted according to penalty rules when the total output power of multiple distributed energy resources is not within a first power constraint range and the power output value is not within a predetermined range, and the penalty rules are rules issued by the distribution system operator; and establishing an objective function based on the electricity sales revenue, loss costs, and penalty fines, with the goal of maximizing profit, wherein the expression of the objective function is: t represents the runtime index, T represents the total runtime, w represents the runtime scenario index, W represents the total runtime scenario, and π represents the total runtime scenario.w C represents the probability of a scenario. sell，t C represents revenue from electricity sales. ESS，w，t C represents the cost of loss. penalty，w，t The penalty is indicated by fines; constraints are imposed on the output state, charging state, discharging state, shutdown state, and charged state of the energy storage system during charging and discharging, resulting in multiple constraints. The energy storage system is one of multiple distributed energy resources; an operational model is constructed based on the objective function and multiple constraints.

[0107] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining the exchange power with the power distribution system, wherein the exchange power is the net output power value when multiple distributed energy resources conduct electricity transactions with the power distribution system, where a net output power value greater than zero indicates the supply of electrical energy to the power distribution system, and a net output power value less than zero indicates the purchase of electrical energy from the power distribution system; calculating the electricity sales revenue based on the exchange power using a first formula, wherein the first formula is: C sell，t =R DA，t ×P VPP-DSO，t R DA，t P represents the electricity price of the previous operating segment in the current operating segment. VPP-DSO，t Represents the exchange power; obtains the charging and discharging power of the energy storage system; calculates the loss cost based on the charging and discharging power using the second formula, where the second formula is: C ESS，w，t =(P ESS_ch，w，t +P ESS_dch，w，t C loss P ESS_ch，w，t P represents the charging power. ESS_dch，w，t C represents the discharge power. loss This represents wear and tear costs; when the total output power is not within the first power constraint range and the power output value is not within the predetermined range, the difference between the total output power and the upper boundary of the first power constraint range is determined as the differential power; based on the differential power, the penalty is calculated using the third formula, where the third formula is: C penalty，w，t =R penalty，t ×P exceed，w，t R penalty，t P represents the unit price of the penalty. exceed，w，t This indicates the differential power.

[0108] According to another aspect of the present invention, a processor is also provided, which is used to run a program, wherein the program executes any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0109] According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, perform any of the above-described virtual power plant operation methods based on power distribution system constraints.

[0110] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0111] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0116] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A virtual power plant operation method based on power distribution system constraints, characterized in that, include: The system receives a first distribution system constraint issued by the distribution system operator. The first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources. The distribution system operator uses this strategy to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range. Obtain historical power distribution system constraints issued by the power distribution system operator within a historical time period, the constraint information of the power distribution system, and power output prediction data of multiple distributed energy resources; The constraint prediction model is used to process the historical distribution system constraints, the constraint information, and the power output prediction data to obtain the second distribution system constraints. The constraint prediction model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical distribution system constraints, sample constraint information, and sample power output prediction data. The initial resource scheduling strategy is adjusted according to the constraints of the second power distribution system to obtain a first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that, under the condition that the power output value meets the predetermined range, makes the total output power exceed the first power constraint range but not exceed the second power constraint range. The first power constraint range is the range of the total output power constrained in the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained in the second power distribution system constraints. The distributed energy resources are scheduled according to the first target resource scheduling strategy.

2. The virtual power plant operation method based on power distribution system constraints according to claim 1, characterized in that, Before receiving the first distribution system constraints issued by the distribution system operator, it also includes: The current status, available capacity, and expected power output data of multiple distributed energy resources are evaluated to obtain evaluation results; An initial resource scheduling strategy is generated based on the evaluation results to schedule multiple distributed energy resources. The initial resource scheduling strategy is sent to the power distribution system operator.

3. The virtual power plant operation method based on power distribution system constraints according to claim 2, characterized in that, Before sending the initial resource scheduling strategy to the power distribution system operator, the method further includes: Establish a collaborative operation framework with the power distribution system operator to enable information exchange with the power distribution system operator through the collaborative operation framework.

4. The virtual power plant operation method based on power distribution system constraints according to claim 1, characterized in that, Also includes: While receiving the first power distribution system constraint issued by the power distribution system operator, the system also receives penalty rules issued by the power distribution system operator, wherein the penalty rules are used to predict penalty fines when the total output power of the multiple distributed energy resources is not within the first power constraint range and the power output value is not within the predetermined range.

5. The virtual power plant operation method based on power distribution system constraints according to claim 1, characterized in that, Also includes: An operational model is constructed, wherein the operational model is used to adjust the scheduling of multiple distributed energy resources; The operating model is solved using an optimization algorithm to obtain the optimal solution of the operating model, wherein the optimal solution is the solution of the objective function in the operating model that maximizes the profit of the virtual power plant; Under the conditions of satisfying the first power distribution system constraints and the second power distribution system constraints, the initial resource scheduling strategy is adjusted according to the optimal solution to obtain the second target resource scheduling strategy; The distributed energy resources are scheduled according to the second target resource scheduling strategy.

6. The virtual power plant operation method based on power distribution system constraints according to claim 5, characterized in that, The operational model is constructed, including: The system obtains current electricity sales revenue, loss costs, and penalties, wherein the electricity sales revenue is the difference between the revenue from supplying electricity to the distribution system and the expenditure from purchasing electricity from the distribution system; the loss costs are the costs incurred by multiple distributed energy resources; and the penalties are penalties predicted according to penalty rules when the total output power of multiple distributed energy resources is not within a first power constraint range and the power output value is not within a predetermined range, and the penalty rules are rules issued by the distribution system operator. Based on the electricity sales revenue, the loss cost, and the penalty, an objective function is established with the goal of maximizing profit. The expression for the objective function is: t represents the running time label, T represents the total number of running times, w represents the running scenario label, W represents the total number of running scenarios, and π w C represents the probability of a scenario. sell，t C represents the revenue from the sale of electricity. ESS，w，t C represents the aforementioned loss cost. penalty，w，t This refers to the penalty or fine mentioned above; Constraints are applied to the output state, charging state, discharging state, stop state, and state of charge of the energy storage system during charging and discharging to obtain multiple constraint conditions, wherein the energy storage system is one of the multiple distributed energy resources; A running model is constructed based on the objective function and the various constraints.

7. The virtual power plant operation method based on power distribution system constraints according to claim 6, characterized in that, Obtain current electricity sales revenue, loss costs, and penalties, including: The exchange power is obtained when power is exchanged with the power distribution system, wherein the exchange power is the net output power value when multiple distributed energy resources conduct power transactions with the power distribution system. When the net output power value is greater than zero, it indicates that the electrical energy is supplied to the power distribution system, and when the net output power value is less than zero, it indicates that the electrical energy is purchased from the power distribution system. The electricity sales revenue is calculated based on the exchange power using a first formula, wherein the first formula is: C sell，t =R DA，t ×P VPP-DSO，t R DA，t P represents the electricity price of the previous operating segment in the current operating segment. VPP-DSO,t This represents the switching power; Obtain the charging power and discharging power of the energy storage system; The loss cost is calculated using a second formula based on the charging power and the discharging power, wherein the second formula is: C ESS，w，t =(P ESS_ch，w，t +P ESS_dch，w，t C loss P ESS_ch，w，t P represents the charging power. ESS_dch，w，t C represents the discharge power. loss Indicates wear and tear costs; If the total output power is not within the first power constraint range and the power output value is not within the predetermined range, the difference between the total output power and the upper boundary of the first power constraint range is determined as the differential power. The penalty is calculated using a third formula based on the difference in power, wherein the third formula is: C penalty，w，t =R penalty，t ×P exceed，w，t R penalty，t P represents the unit price of the penalty. exceed，w，t This represents the difference in power.

8. A virtual power plant operation device based on power distribution system constraints, characterized in that, include: The first receiving unit is configured to receive a first distribution system constraint issued by the distribution system operator. The first distribution system constraint is a constraint condition calculated by the distribution system operator based on the initial resource scheduling strategy sent by the virtual power plant. The initial resource scheduling strategy is a strategy for scheduling multiple distributed energy resources. The distribution system operator is used to constrain the total output power of the multiple distributed energy resources so that the power output value of the distribution system is within a predetermined range. The first acquisition unit is used to acquire historical power distribution system constraints issued by the power distribution system operator within a historical time period, the constraint information of the power distribution system, and power output prediction data of multiple distributed energy resources. The second acquisition unit is used to process the historical power distribution system constraints, the constraint information, and the power output prediction data using a constraint prediction model to obtain the second power distribution system constraints of the virtual power plant. The constraint prediction model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample input data and sample second power distribution system constraints corresponding to the sample input data. The sample input data includes: sample historical power distribution system constraints, sample constraint information, and sample power output prediction data. The third acquisition unit is used to adjust the initial resource scheduling strategy according to the second power distribution system constraints to obtain a first target resource scheduling strategy. The first target resource scheduling strategy is a strategy that, under the condition that the power output value meets the predetermined range, makes the total output power exceed the first power constraint range but not exceed the second power constraint range. The first power constraint range is the range of the total output power constrained in the first power distribution system constraints, and the second power constraint range is the range of the total output power constrained in the second power distribution system constraints. The first scheduling unit is used to schedule multiple distributed energy resources according to the first target resource scheduling strategy.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program executes the virtual power plant operation method based on power distribution system constraints as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, the virtual power plant operation method based on power distribution system constraints as described in any one of claims 1 to 7 is performed.

Images