Power supply type virtual power plant active feasible region aggregation method

Abstract

The invention relates to the technical field of power systems, in particular to a power supply type virtual power plant active feasible region aggregation method, which comprises the following steps: collecting distributed power supplies, and dividing the distributed power supplies into a renewable resource class and a conventional unit class; sequentially establishing a renewable resource active feasible region model and a conventional unit active feasible region model according to the renewable resource class and the conventional unit class; in combination with the renewable resource active feasible region model and the conventional unit active feasible region model, respectively constructing an aggregation active upper and lower bound optimization model and a climbing rate upper and lower bound optimization model of the power supply type virtual power plant; and solving the optimization model to obtain aggregated active power upper and lower bounds and climbing rate upper and lower bounds of the virtual power plant, and generating an active feasible region of the power supply type virtual power plant. According to the active feasible region aggregation method for the power supply type virtual power plant, aggregation calculation time consumption can be reduced, a simple and reliable aggregation model is provided for the power supply type virtual power plant to participate in power grid optimization scheduling, and the overall calculation efficiency of scheduling model solving is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for aggregating active feasible domains of power supply type virtual power plants. Background Art

[0002] With the rapid development of renewable resource technology and the transformation of resource structure, the penetration rate of distributed power sources in the power system continues to increase, which makes the optimal dispatch of power grids face unprecedented challenges. In view of the characteristics of distributed power sources such as wide geographical distribution, large number of equipment and strong heterogeneity, building a power supply type virtual power plant is an effective solution. The existing method usually adopts the traditional Minkowski sum operation, which theoretically provides a strict mathematical framework for the calculation of the active feasible domain of power supply type virtual power plants. However, when dealing with high-dimensional dynamic resource space, the analytical solution is subject to the spatiotemporal coupling characteristics of heterogeneous operation constraints and will face the computational bottleneck of dimensional exponential explosion.

[0003] In recent years, some studies have been conducted on the approximate calculation of the Minkowski sum, which has been proven to be effective in solving some of the above challenges; however, the approximate method still has certain limitations in terms of the accuracy of the solution, and presents a high degree of complexity in the calculation process, and does not provide a clear architecture for model characterization. At the same time, the virtual power plant aggregation modeling methods proposed in existing studies mainly focus on the cluster equivalence of a single type of power source or homogeneous distributed resources, and lack a method for unified aggregation of heterogeneous distributed power sources. Summary of the invention

[0004] In order to solve the technical problems that the existing feasible domain aggregation method is complex and slow in calculation and cannot uniformly aggregate heterogeneous distributed power sources, the purpose of the present invention is to provide a method for aggregating the active feasible domain of a power supply type virtual power plant. The technical solution adopted is as follows: Collect distributed power sources and classify them into renewable resources and conventional units; According to the renewable resource category and the conventional unit category, the renewable resource active feasible domain model and the conventional unit active feasible domain model are established in turn; Combining the feasible domain model of renewable resource active power and the feasible domain model of conventional units active power, the optimization models of aggregated active power upper and lower bounds and ramp rate upper and lower bounds of power supply type virtual power plant are constructed respectively. Solving the optimization model of the upper and lower bounds of aggregated active power and the upper and lower bounds of ramp rate corresponds to obtaining the upper and lower bounds of aggregated active power and the upper and lower bounds of ramp rate of the virtual power plant, and generating the active feasible domain of the power supply type virtual power plant.

[0005] Preferably, distributed power sources are collected and divided into renewable resource classes and conventional unit classes, including: obtaining massive distributed power source data from various distributed power source devices, and dividing them into renewable resource classes and conventional unit classes according to operating characteristics and constraints.

[0006] Preferably, according to the renewable resource category and the conventional unit category, a renewable resource active feasible domain model and a conventional unit active feasible domain model are sequentially established, including: The renewable resources include wind turbine power generation and photovoltaic power generation, and the conventional units include diesel generators and gas turbines; According to the adjustable range of active power of wind turbine power generation and photovoltaic power generation, the active power feasible domain model of wind turbine power generation and photovoltaic power generation is constructed; According to the adjustable active power range and ramp rate constraints of diesel generators and gas turbines, the active power feasible domain models of diesel generators and gas turbines are constructed.

[0007] Preferably, an active power feasible domain model is established according to the adjustable active power range of wind turbine power generation and photovoltaic power generation, and the corresponding calculation formula is:

[0008] in, express The active power output of photovoltaic at all times; , Respectively The upper and lower limits of photovoltaic active power at all times; express Active output of the fan at all times; , Respectively The upper and lower limits of the active power of the fan at all times.

[0009] Preferably, an active power feasible domain model is established according to the active power adjustable range and ramp rate constraints of the diesel generator and the gas turbine, and the corresponding calculation formula is:

[0010] in, , Respectively time, Active power output of diesel generator at all times; , Respectively The upper and lower limits of the active power of the diesel generator at all times; , Respectively The upper and lower limits of the ramp rate of the diesel generator at any given moment; , Respectively time, Active power output of gas turbine at any moment; , Respectively The upper and lower limits of the active power of the gas turbine at each moment; , Respectively The upper and lower limits of the gas turbine's ramp rate at this moment.

[0011] Preferably, the feasible domain model of active power of renewable resources and the feasible domain model of active power of conventional units are combined to respectively construct the upper and lower bounds of aggregated active power and the upper and lower bounds of ramp rate of the power supply type virtual power plant, including: Construct and determine the upper limit of the active power of the power supply type virtual power plant , Nether and the upper bound of the climbing rate , Nether The corresponding optimization model is: Used to find the upper bound of aggregate power The optimization model is:

[0012] Used to find the lower bound of aggregate power The optimization model is:

[0013] Used to find the upper bound of the aggregate climbing rate The optimization model is:

[0014] Used to find the lower bound of the aggregate climbing rate The optimization model is:

[0015] in, Respectively time, Active power output of the moment-to-moment power supply virtual power plant; , , , They represent the aggregated resource quantities of photovoltaics, wind turbines, diesel generators, and gas turbines respectively; Indicates the period of resource aggregation; Indicates Group photovoltaic resources in Always make contributions; Indicates Group fan resources in Always make contributions; , Respectively represent Group fan resources in The meritorious upper and lower bounds of the moment; Respectively represent Diesel generator set time, Always make contributions; , Respectively represent Diesel generator set The meritorious upper and lower bounds of the moment; , Respectively represent Diesel generator set The upper and lower bounds of the climbing rate at the time; Respectively represent Gas turbine in time, Always make contributions; , Respectively represent Gas turbine in The meritorious upper and lower bounds of the moment; , Respectively represent Gas turbine in The upper and lower bounds of the climbing rate at the time.

[0016] Preferably, solving the optimization model of aggregated active power upper and lower bounds and ramp rate upper and lower bounds corresponds to obtaining the aggregated active power upper and lower bounds and ramp rate upper and lower bounds of the virtual power plant, and generating the active feasible domain of the power supply type virtual power plant. The corresponding calculation formula is:

[0017] in, express Active power output of the virtual power plant with instant power supply; , Respectively The upper and lower power limits of the moment-to-moment power supply virtual power plant; , Respectively The upper and lower bounds of the ramp rate of the moment-to-moment power supply type virtual power plant.

[0018] The present invention has the following beneficial effects: This application collects distributed power sources and divides them into renewable resource categories and conventional unit categories. On this basis, renewable resource active feasible domain models and conventional unit active feasible domain models are established in turn according to renewable resource categories and conventional unit categories. Combined with the renewable resource active feasible domain model and the conventional unit active feasible domain model, the optimization models of aggregated active upper and lower bounds and ramp rate upper and lower bounds of the power supply type virtual power plant are constructed respectively. Finally, by solving the above optimization model, the aggregated active upper and lower bounds and ramp rate upper and lower bounds of the virtual power plant are obtained to generate the active feasible domain of the power supply type virtual power plant. The present invention reduces the time consumption of aggregation calculation in the solution process, and can provide a simple and reliable aggregation model for the power supply type virtual power plant to participate in the optimal scheduling of the power grid, thereby improving the overall computational efficiency of the solution of the optimal scheduling model of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flowchart of the steps of a method for aggregating active feasible domains of a power supply type virtual power plant provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the active feasible domain aggregation method of a power supply type virtual power plant proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0022] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0023] The specific scheme of the method for aggregating the active feasible domain of a power supply type virtual power plant provided by the present invention is described in detail below with reference to the accompanying drawings.

[0024] See also Figure 1 , which shows a flowchart of a method for aggregating active feasible domains of a power supply type virtual power plant provided by an embodiment of the present invention, the method comprising: Step S1: Collect distributed power sources and divide them into renewable resources and conventional units; Step S2: establishing a renewable resource active feasible domain model and a conventional unit active feasible domain model in turn according to the renewable resource category and the conventional unit category; Step S3: Combining the feasible domain model of renewable resource active power and the feasible domain model of conventional units active power, respectively constructing the upper and lower bounds of aggregated active power and the upper and lower bounds of ramp rate of the power supply type virtual power plant; Step S4: Solve the optimization model of aggregated active power upper and lower bounds and ramp rate upper and lower bounds to obtain the aggregated active power upper and lower bounds and ramp rate upper and lower bounds of the virtual power plant, and generate the active power feasible domain of the power supply type virtual power plant.

[0025] To better illustrate, in recent years, with the deepening of global sustainable development goals, the resource structure of the power system is undergoing a historic transformation from fossil energy-dominated to renewable resources-centered. Renewable resources, mainly wind and solar energy, and various distributed power sources have significant characteristics such as large quantity, wide spatial distribution, and weak single-unit regulation capabilities. This feature makes the traditional centralized dispatching model unsustainable. It is urgent to use resource aggregation technology to build distributed power clusters with geographical dispersion and different output characteristics into virtual power plants (VPPs) with scale regulation capabilities.

[0026] It can be explained that this aggregation model of virtual power plants can integrate massive fragmented resources into dispatchable units through the cluster effect, improve the flexibility and dispatchability of the power system, help coordinate various types of distributed power sources, and provide key support for building a new power system.

[0027] To illustrate, step S1 includes: obtaining massive distributed power data from various distributed power devices, and dividing them into renewable resource categories and conventional unit categories based on operating characteristics and constraints; this step provides more diversified power data for subsequent analysis.

[0028] Furthermore, step S2 includes: Step S21: Renewable resources include wind turbine power generation and photovoltaic power generation, and conventional units include diesel generators and gas turbines; It can be explained that the renewable resources category is represented by wind power generation and photovoltaic power generation, and its own constraints do not include ramp rate constraints, that is, this type of power supply can increase or decrease its output power relatively freely when adjusting the power generation without being affected by specific technical restrictions; the conventional units category is represented by diesel generators and gas turbines, and its own constraints include ramp rate constraints, that is, this type of power supply must follow certain rate limits when adjusting the power generation to ensure the stable operation of the power supply and extend its service life, and avoid damage to the power generation equipment or performance degradation due to excessively rapid power changes.

[0029] Step S22: construct an active feasible domain model of wind turbine power generation and photovoltaic power generation according to the adjustable active power range of wind turbine power generation and photovoltaic power generation. The corresponding calculation formula is:

[0030] in, express The active power output of photovoltaic at all times; , Respectively The upper and lower limits of photovoltaic active power at any moment; express Active output of the fan at all times; , Respectively The upper and lower limits of the active power of the fan at all times.

[0031] Step S23: According to the adjustable active power range and ramp rate constraints of the diesel generator and the gas turbine, an active power feasible domain model is established. The corresponding calculation formula is:

[0032] in, , Respectively time, Active power output of diesel generator at all times; , Respectively The upper and lower limits of the active power of the diesel generator at all times; , Respectively The upper and lower limits of the ramp rate of the diesel generator at any given moment; , Respectively time, Active power output of gas turbine at any moment; , Respectively The upper and lower limits of the active power of the gas turbine at each moment; , Respectively The upper and lower limits of the gas turbine's ramp rate at this moment.

[0033] Furthermore, step S3 includes: Construct the upper bound of the active power of the power supply type virtual power plant in turn , Nether and the upper bound of the ramp rate , Nether The corresponding four optimization models are: Used to find the upper bound of aggregate power The optimization model is:

[0034] Used to find the lower bound of aggregate power The optimization model is:

[0035] Used to find the upper bound of the aggregate climbing rate The optimization model is:

[0036] Used to find the lower bound of the aggregate climbing rate The optimization model is:

[0037] in, Respectively time, Active power output of the moment-to-moment power supply virtual power plant; , , , Represents the aggregated resource quantity of photovoltaic, wind turbine, diesel generator and gas turbine respectively, Indicates the period of resource aggregation; Indicates Group photovoltaic resources in Always make contributions; Indicates Group fan resources in Always make contributions; , Respectively represent Group fan resources in The meritorious upper and lower bounds of the moment; Respectively represent Diesel generator set time, Always make contributions; , Respectively represent Diesel generator set The meritorious upper and lower bounds of the moment; , Respectively represent Diesel generator set The upper and lower bounds of the climbing rate at the time; Respectively represent Gas turbine in time, Always make contributions; , Respectively represent Gas turbine in The meritorious upper and lower bounds of the moment; , Respectively represent Gas turbine in The upper and lower bounds of the climbing rate at the time.

[0038] Further, in step S4, the optimization model of aggregated active power upper and lower bounds and ramp rate upper and lower bounds is solved to obtain the aggregated active power upper and lower bounds and ramp rate upper and lower bounds of the virtual power plant, and the active power feasible domain of the power supply type virtual power plant is generated. The corresponding calculation formula is:

[0039] in, express Active power output of the virtual power plant with instant power supply; , Respectively The upper and lower power limits of the moment-to-moment power supply virtual power plant; , Respectively The upper and lower bounds of the ramp rate of the moment-to-moment power supply type virtual power plant.

[0040] It can be understood that in this embodiment, distributed power sources are collected and divided into renewable resource classes and conventional unit classes. On this basis, the renewable resource active feasible domain model and the conventional unit active feasible domain model are established in turn according to the renewable resource class and the conventional unit class. Combined with the renewable resource active feasible domain model and the conventional unit active feasible domain model, the aggregated active upper and lower bounds and the climbing rate upper and lower bounds optimization models of the power type virtual power plant are constructed respectively. Finally, by solving the above optimization model, the aggregated active upper and lower bounds and the climbing rate upper and lower bounds of the virtual power plant are obtained, and the active feasible domain of the power type virtual power plant is generated. This example reduces the time consumption of aggregation calculations in the solution process, avoids the dimensionality curse problem faced by the traditional Minkowski sum in the aggregation process, and can provide a simple and reliable aggregation model for the power type virtual power plant to participate in the optimal scheduling of the power grid, thereby improving the overall computational efficiency of the solution of the power grid optimal scheduling model.

[0041] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0042] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for aggregating active feasible domains of a power supply type virtual power plant, characterized in that: The method comprises: Collect distributed power sources and classify them into renewable resources and conventional units; According to the renewable resource category and the conventional unit category, the renewable resource active feasible domain model and the conventional unit active feasible domain model are established in turn; Combining the feasible domain model of renewable resource active power and the feasible domain model of conventional units active power, the optimization models of aggregated active power upper and lower bounds and ramp rate upper and lower bounds of power supply type virtual power plant are constructed respectively. Solving the optimization model of the upper and lower bounds of aggregated active power and the upper and lower bounds of ramp rate corresponds to obtaining the upper and lower bounds of aggregated active power and the upper and lower bounds of ramp rate of the virtual power plant, and generating the active feasible domain of the power supply type virtual power plant.

2. The method for aggregating active feasible domain of a power supply type virtual power plant according to claim 1, characterized in that: Collect distributed power sources and classify them into renewable resources and conventional units, including: obtaining massive distributed power data from various distributed power devices, and classifying them into renewable resources and conventional units according to operating characteristics and constraints.

3. The method for aggregating active feasible domain of a power supply type virtual power plant according to claim 1, characterized in that: According to the renewable resource category and the conventional unit category, the renewable resource active feasible domain model and the conventional unit active feasible domain model are established in turn, including: The renewable resources include wind turbine power generation and photovoltaic power generation, and the conventional units include diesel generators and gas turbines; According to the adjustable range of active power of wind turbine power generation and photovoltaic power generation, the active power feasible domain model of wind turbine power generation and photovoltaic power generation is constructed; According to the adjustable active power range and ramp rate constraints of diesel generators and gas turbines, the active power feasible domain models of diesel generators and gas turbines are constructed.

4. The method for aggregating active feasible domain of a power supply type virtual power plant according to claim 3 is characterized in that: According to the adjustable range of active power of wind turbine power generation and photovoltaic power generation, the active power feasible domain model is established, and the corresponding calculation formula is: ； in, express The active power output of photovoltaic at all times; , Respectively The upper and lower limits of photovoltaic active power at all times; express Active output of the fan at all times; , Respectively The upper and lower limits of the active power of the fan at all times.

5. The method for aggregating active feasible domain of a power supply type virtual power plant according to claim 3 is characterized in that: According to the adjustable active power range and ramp rate constraints of diesel generators and gas turbines, the active power feasible domain model is established, and the corresponding calculation formula is: ； in, , Respectively time, Active power output of diesel generator at all times; , Respectively The upper and lower limits of the active power of the diesel generator at all times; , Respectively The upper and lower limits of the ramp rate of the diesel generator at any given moment; , Respectively time, Active power output of gas turbine at any moment; , Respectively The upper and lower limits of the active power of the gas turbine at each moment; , Respectively The upper and lower limits of the gas turbine's ramp rate at this moment.

6. The method for aggregating active feasible domain of a power supply type virtual power plant according to claim 1, characterized in that: Combining the feasible domain model of renewable resource active power and the feasible domain model of conventional units active power, the optimization models of aggregated active power upper and lower bounds and ramp rate upper and lower bounds of power supply type virtual power plant are constructed respectively, including: Construct and determine the upper limit of the active power of the power supply type virtual power plant , Nether and the upper bound of the climbing rate , Nether The corresponding optimization model is: Used to find the upper bound of aggregate power The optimization model is: ； Used to find the lower bound of aggregate power The optimization model is: ； Used to find the upper bound of the aggregate climbing rate The optimization model is: ； Used to find the lower bound of the aggregate climbing rate The optimization model is: ； in, Respectively time, Active power output of the moment-to-moment power supply virtual power plant; , , , They represent the aggregated resource quantities of photovoltaics, wind turbines, diesel generators, and gas turbines respectively; Indicates the period of resource aggregation; Indicates Group photovoltaic resources in Always make contributions; Indicates Group fan resources in Always make contributions; , Respectively represent Group fan resources in The meritorious upper and lower bounds of the moment; Respectively represent Diesel generator set time, Always make contributions; , Respectively represent Diesel generator set The meritorious upper and lower bounds of the moment; , Respectively represent Diesel generator set The upper and lower bounds of the climbing rate at the time; Respectively represent Gas turbine in time, Always make contributions; , Respectively represent Gas turbine in The meritorious upper and lower bounds of the moment; , Respectively represent Gas turbine in The upper and lower bounds of the climbing rate at the time.

7. The method for aggregating active feasible domain of a power supply type virtual power plant according to claim 1, characterized in that: Solving the optimization model of aggregated active power upper and lower bounds and ramp rate upper and lower bounds can obtain the aggregated active power upper and lower bounds and ramp rate upper and lower bounds of the virtual power plant, and generate the active feasible domain of the power supply type virtual power plant. The corresponding calculation formula is: ； in, express Active power output of the moment-to-moment power supply virtual power plant; , Respectively The upper and lower power limits of the moment-to-moment power supply virtual power plant; , Respectively The upper and lower bounds of the ramp rate of the moment-to-moment power supply type virtual power plant.

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