Method for measuring and calculating scale diffusion trend of regional virtual power plant for power grid planning

Abstract

The invention discloses a method for measuring and calculating the scale diffusion trend of a regional virtual power plant for power grid planning, and relates to the technical field of novel power systems. According to the method, after the data representing the scale diffusion condition of the virtual power plant in the target area, namely the target data, are obtained, the target data are input into the measurement and calculation model, and the scale diffusion result of the virtual power plant in the target area is obtained; the measurement and calculation model is a first model, a second model and a third model which are constructed on the basis of pre-selected influence parameters and are used for representing the regional power installation condition, the regional virtual power plant investment operation condition and the regional power demand condition. The built measurement and calculation model fully considers key influence factors such as internal operation characteristics of the virtual power plant and external regional power demand, power grid planning and the like, so that the internal mechanism and evolution law of regional virtual power plant scale diffusion are disclosed, and the economical efficiency of virtual power plant investment construction operation is analyzed. And the power grid is helped to accurately evaluate the influence of virtual power plant access on the power grid planning scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of new power systems, and more specifically, to a method for calculating the scale diffusion trend of regional virtual power plants for power grid planning. Background Art

[0002] In recent years, the integration of massive, heterogeneous distributed energy resources into the power grid has increased the urgency and importance of integrated power generation, grid-load, and storage. Virtual power plants, which aggregate flexible resources to provide system access, leverage the internet and modern information and communications technologies. Within the traditional physical grid architecture, they integrate distributed power sources, energy storage, loads, and other resources dispersed across the grid for collaborative optimization, operational control, and market transactions. This approach can further tap the potential of distributed resources and is a key method for promoting the organic interaction between power generation, grid-load, and storage, and enhancing system flexibility.

[0003] However, due to factors such as geography, climate, and resource composition, virtual power plants (VPPs) vary widely across regions, with significant variations in development paths, scale, and application scenarios. This makes it difficult to effectively measure their scale and diffusion trends, making it difficult for power grids to assess the effectiveness of their operational management and control. Therefore, it is necessary to analyze the economic viability of VPP investment, construction, and operation to help power grids accurately assess the impact of VPP integration on grid planning. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for calculating the scale diffusion trend of regional virtual power plants for power grid planning, so as to solve the problem that the power grid cannot accurately evaluate the impact of virtual power plant access on the power grid planning scheme due to the inability to analyze the economic feasibility of virtual power plant investment, construction and operation.

[0005] The present invention provides a method for calculating the scale diffusion trend of regional virtual power plants for power grid planning, comprising:

[0006] Acquiring target data, wherein the target data is data representing the scale and spread of virtual power plants in a target area;

[0007] Inputting the target data into the calculation model to obtain the results of the scale diffusion of virtual power plants in the target area;

[0008] The calculation model is a system dynamics model constructed based on preselected influencing parameters, and the system dynamics model includes: a first model for characterizing the regional power installed capacity, a second model for characterizing the investment and operation of the regional virtual power plant, and a third model for characterizing the regional power demand;

[0009] The impact parameter is a parameter whose impact on the scale diffusion of virtual power plants exceeds a preset impact threshold.

[0010] In a preferred embodiment, constructing the first model includes:

[0011] Obtaining the influencing parameter affecting the installed power capacity in the region as a first influencing parameter;

[0012] establishing an association relationship between the first influencing parameters;

[0013] Based on the correlation between the first influencing parameters, a system dynamics flow diagram equation for characterizing the installed power capacity in the region is constructed as the first model.

[0014] In a preferred solution, the first influencing parameter includes:

[0015] Total regional installed capacity, change in regional installed capacity, scale of thermal power installed capacity, change in thermal power installed capacity, proportion of thermal power installed capacity phased out, thermal power market share, virtual power plant market share, capacity added from regional grid upgrades, additional length of transmission lines, investment in transmission line renovation, number of new substations, and costs of substation renovation and upgrades;

[0016] The total installed capacity of the region is determined by the initial value of the regional installed capacity, the newly added capacity due to the upgrade of the regional power grid, the newly added installed capacity of virtual power plants, and the change in the installed capacity of thermal power plants; the change in the installed capacity of thermal power plants is determined by the scale of thermal power plants and the proportion of thermal power plants phased out; the number of new substations is determined by the cost of transformation and upgrading of the substations and the unit construction cost of the substations;

[0017] The additional length of the transmission line is determined by the investment in the transformation of the transmission line and the unit cost of the transmission line reconstruction.

[0018] In a preferred embodiment, the first model is:

[0019] Regional total installed capacity = initial value of regional total installed capacity + correction parameter for regional installed capacity change;

[0020] Change in regional installed capacity = change in thermal power installed capacity + new installed capacity of virtual power plants;

[0021] Change in thermal power installed capacity = 0.5 × newly added capacity due to regional power grid upgrades - 0.1 × thermal power installed capacity × proportion of thermal power installed capacity eliminated;

[0022] Thermal power installed capacity = initial value of thermal power installed capacity + correction parameter for changes in thermal power installed capacity;

[0023] Thermal power market share = thermal power installed capacity / regional total installed capacity;

[0024] Number of new substations = cost of substation renovation and upgrade / unit construction cost of substation;

[0025] Newly added length of transmission line = transmission line reconstruction investment / unit cost of transmission line reconstruction.

[0026] In a preferred embodiment, constructing the second model includes:

[0027] Obtaining the influencing parameter affecting the investment and operation of the virtual power plant in the region as a second influencing parameter;

[0028] establishing an association relationship between the second influencing parameters;

[0029] Based on the correlation between the second influencing parameters, a system dynamics flow diagram equation for characterizing the investment and operation status of the virtual power plant in the region is constructed as the second model.

[0030] In a preferred solution, the second influencing parameter includes:

[0031] Cumulative R&D investment, cumulative installed capacity of virtual power plants, investment in new installed capacity of virtual power plants, the impact of R&D investment on distributed energy generation and energy storage systems, total cost of distributed energy generation, total cost of energy storage systems, and total cost of virtual power plants;

[0032] Among them, the total cost of the virtual power plant is determined by the total cost of distributed energy generation, the total cost of energy storage system operation and the demand response call cost; the total cost of distributed energy generation is determined by the distributed photovoltaic cost, distributed wind power cost and distributed gas power generation cost; the total cost of the energy storage system is determined by the initial cost of energy storage system operation; the demand response call cost is determined by the demand response call ratio and the demand response call price; the cumulative R&D investment is determined by the virtual power plant profit and the proportion of new R&D investment; the scale diffusion of the virtual power plant is determined by the new installed capacity investment of the virtual power plant and the investment per unit installed capacity.

[0033] In a preferred embodiment, the second model is:

[0034] Total cost of a virtual power plant = distributed wind power cost + distributed photovoltaic cost + distributed gas power generation cost + total energy storage system cost + demand response call cost;

[0035] Distributed wind power cost = virtual power plant power generation × distributed wind power generation ratio × distributed wind power unit electricity cost × (1-the impact of R&D investment on distributed wind power generation cost);

[0036] Distributed PV cost = virtual power plant power generation × distributed PV power generation ratio × distributed PV unit electricity cost × (1-the impact of R&D investment on distributed PV power generation cost);

[0037] Distributed gas-fired power generation cost = virtual power plant power generation × distributed gas-fired power generation ratio × distributed gas-fired power generation unit electricity cost × (1-the impact of R&D investment on distributed gas-fired power generation cost);

[0038] Total cost of energy storage system = initial cost of energy storage system operation × (1-the impact of R&D investment on energy storage system operating cost);

[0039] Demand response call cost = virtual power plant power generation × demand response call ratio × demand response call price;

[0040] Cumulative R&D investment = initial R&D investment value + correction parameter for new R&D investment;

[0041] New R&D investment = virtual power plant profit × new R&D investment ratio;

[0042] Virtual power plant scale diffusion = initial value of virtual power plant installed capacity + correction parameter of virtual power plant's newly installed capacity;

[0043] New installed capacity of virtual power plants = investment in new installed capacity of virtual power plants / investment per unit installed capacity + 0.5 × regional power grid upgrade capacity.

[0044] In a preferred embodiment, constructing the third model includes:

[0045] Obtaining the influencing parameter affecting the power demand in the region as a third influencing parameter;

[0046] Establishing an association relationship between the third influencing parameters;

[0047] Based on the correlation between the third influencing parameters, a system dynamics flow diagram equation for characterizing the regional power demand situation is constructed as the third model.

[0048] In a preferred solution, the third influencing parameter includes:

[0049] Regional GDP, regional population, regional GDP growth rate, regional population growth rate, electricity sales of virtual power plants, electricity sales of thermal power plants, regional total social electricity demand, electricity consumption of the primary industry, electricity consumption of the secondary industry, electricity consumption of the tertiary industry, regional residential electricity consumption, virtual power plant electricity sales revenue, virtual power plant profits, and virtual power plant electricity sales subsidies;

[0050] Among them, the profit of the virtual power plant is determined by electricity sales revenue, subsidies and costs; the regional total social electricity demand is determined by the first industry electricity consumption, the second industry electricity consumption, the tertiary industry electricity consumption and the residential electricity consumption; the first industry electricity consumption, the second industry electricity consumption and the tertiary industry electricity consumption are determined by the output value of each industry and the electricity consumption intensity of each industry; the residential electricity consumption in the region is determined by the total population of the region and the per capita residential electricity consumption; the electricity sales volume of the virtual power plant is determined by the virtual power plant market share and the regional total social electricity demand.

[0051] In a preferred embodiment, the third model is:

[0052] Virtual power plant electricity sales subsidy = regional virtual power plant electricity sales × virtual power plant unit electricity sales subsidy;

[0053] Virtual power plant profit = virtual power plant electricity sales revenue + virtual power plant electricity sales subsidy - virtual power plant total cost;

[0054] Total electricity consumption in a region = electricity consumption in the primary industry + electricity consumption in the secondary industry + electricity consumption in the tertiary industry + electricity consumption in the region's residents;

[0055] Electricity consumption of primary industry = regional GDP × proportion of primary industry × electricity consumption intensity of primary industry;

[0056] Secondary industry electricity consumption = regional GDP × secondary industry share × secondary industry electricity intensity;

[0057] Electricity consumption of the tertiary industry = regional GDP × proportion of the tertiary industry × electricity intensity of the tertiary industry;

[0058] Regional GDP = initial regional GDP + (regional GDP growth rate × regional GDP) correction parameter;

[0059] Regional residential electricity consumption = regional population × per capita residential electricity consumption;

[0060] Total regional population = initial regional population + correction parameter of (regional population growth rate × total regional population);

[0061] The amount of electricity sold by a virtual power plant = the total social electricity demand in the region × the market share of the virtual power plant ÷ (1-line loss rate).

[0062] In order to achieve the above-mentioned purpose, the present invention provides a method for calculating the scale diffusion trend of regional virtual power plants for power grid planning. After obtaining data characterizing the scale diffusion of virtual power plants in the target area, namely target data, the target data is input into a calculation model to obtain the results of the scale diffusion of virtual power plants in the target area. The calculation model is a system dynamics model constructed based on preselected influencing parameters, including: a first model for characterizing the regional power installed capacity, a second model for characterizing the investment and operation of regional virtual power plants, and a third model for characterizing the regional power demand. The influencing parameters are parameters whose impact on the scale diffusion of virtual power plants exceeds a preset impact threshold. The constructed calculation model fully considers the internal operating characteristics of virtual power plants and key influencing factors such as external regional power demand and power grid planning, thereby revealing the internal mechanism and evolution law of the scale diffusion of regional virtual power plants, analyzing the economic feasibility of investment, construction and operation of virtual power plants, and helping power grids accurately evaluate the impact of virtual power plant access on power grid planning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of a method for calculating the scale diffusion trend of regional virtual power plants for power grid planning is provided for the implementation of the present invention.

[0064] Figure 2 A system dynamics flow diagram for characterizing regional power installed capacity is provided for the implementation of the present invention.

[0065] Figure 3 A system dynamics flow diagram for characterizing the investment and operation status of regional virtual power plants is provided for the implementation of the present invention.

[0066] Figure 4 A system dynamics flow diagram for characterizing regional power demand conditions is provided for the implementation of the present invention.

[0067] Figure 5 A specific operational flow chart of the method for calculating the regional virtual power plant scale diffusion trend for power grid planning provided for the implementation of the present invention.

[0068] Figure 6 The method for calculating the regional virtual power plant scale diffusion trend for power grid planning provided by the present invention is used to obtain the calculation results of the virtual power plant scale diffusion situation in a certain city.

[0069] Figure 7 The market share calculation result of virtual power plants in a certain city is obtained by using the method for calculating the scale diffusion trend of regional virtual power plants for power grid planning provided by the present invention.

[0070] Figure 8 This is a structural diagram of a regional virtual power plant scale diffusion estimation system provided by an embodiment of the present invention.

[0071] Figure 9 A structural diagram of another regional virtual power plant scale diffusion estimation system provided in an embodiment of the present invention.

[0072] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0074] The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning provided by the present invention is executed on an electronic device, which can be a smart terminal device such as a laptop, personal computer, and tablet computer, or a server on the network side.

[0075] like Figure 1 As shown, the method for calculating the scale diffusion trend of regional virtual power plants for power grid planning provided by an embodiment of the present invention mainly includes the following steps:

[0076] 110. Get target data.

[0077] Among them, the target data is data that characterizes the scale and spread of virtual power plants in the target area.

[0078] 120. Input the target data into the calculation model to obtain the results of the scale diffusion of virtual power plants in the target area.

[0079] The calculation model is a system dynamics model constructed based on preselected influencing parameters. The system dynamics model includes: a first model for characterizing the regional power installed capacity, a second model for characterizing the regional virtual power plant investment and operation, and a third model for characterizing the regional power demand.

[0080] The impact parameter is the parameter whose impact on the scale diffusion of virtual power plants exceeds the preset impact threshold.

[0081] Specifically, the measurement model can be simulated through the system dynamics simulation software Vensim to obtain the results of the scale diffusion of virtual power plants in the target area, such as: the annual new installed capacity of virtual power plants, the regional scale diffusion of virtual power plants and the changes in the regional virtual power plant market share.

[0082] In some possible embodiments, by combining the regional installed power capacity, virtual power plant investment and operation conditions, and power demand conditions, and sorting out influencing factors such as regional economic development, virtual power plant policies, and power grid planning, the key factors affecting the scale diffusion of virtual power plants, namely, influencing parameters, are screened out.

[0083] Furthermore, after screening out the influencing parameters, by setting the boundaries of the virtual power plant scale diffusion system, the virtual power plant scale diffusion measurement system can be divided into a regional power installed capacity scale diffusion subsystem, a virtual power plant investment and operation subsystem, and a regional power supply and demand subsystem, which respectively correspond to the measurement models involved in the above embodiments, including the first model for characterizing the regional power installed capacity situation, the second model for characterizing the regional virtual power plant investment and operation situation, and the third model for characterizing the regional power demand situation.

[0084] Therefore, the method for calculating the regional virtual power plant scale diffusion trend for power grid planning provided by the embodiment of the present invention also includes the step of constructing a calculation model, that is, it includes three sub-steps of constructing a first model, a second model and a third model.

[0085] In this embodiment, by fully considering the internal operating characteristics of the virtual power plant and key influencing factors such as external regional power demand and grid planning, the virtual power plant scale diffusion system is divided into a regional power installed capacity subsystem, a virtual power plant investment and operation subsystem, and a regional power demand subsystem, and then a regional virtual power plant scale diffusion system dynamic model is established. After obtaining the target data, the target data is input into the measurement model to obtain the results that reveal the internal mechanism and evolution law of the virtual power plant scale diffusion in the target area, thereby achieving a reliable analysis of the economic feasibility of investment, construction and operation of virtual power plants in the target area, and helping the power grid to accurately evaluate the impact of virtual power plant access on the grid planning scheme.

[0086] Based on the content of the above embodiment, constructing the first model includes:

[0087] Obtaining an influencing parameter affecting regional power installed capacity as a first influencing parameter;

[0088] Establishing a correlation relationship between first influencing parameters;

[0089] Based on the correlation between the first influencing parameters, a system dynamics flow diagram equation for characterizing the regional power installed capacity is constructed as the first model.

[0090] In this embodiment, the influencing parameter affecting the installed power capacity of the region is first obtained as the first influencing parameter, and then Figure 2 As shown, the correlation between the first influencing parameters is established, thereby obtaining the system dynamics flow diagram equation for characterizing the regional power installed capacity, that is, the first model. The first model can then be used to deduce the changes in the total installed capacity, market share of virtual power plants, and installed capacity diffusion of thermal power plants in the target region.

[0091] Based on the content of the above embodiment, the influencing parameters affecting the regional power installed capacity, that is, the first influencing parameters include: regional total installed capacity, regional installed capacity changes, thermal power installed capacity scale, thermal power installed capacity changes, thermal power installed capacity elimination ratio, thermal power market share, virtual power plant market share, regional power grid upgrade new capacity, new transmission line length, transmission line transformation investment, new number of substations, and substation transformation and upgrade costs.

[0092] The total installed capacity of a region is determined by the initial value of the regional installed capacity, the newly added capacity due to regional power grid upgrades, the newly added capacity of virtual power plants, and the change in thermal power installed capacity; the change in thermal power installed capacity is determined by the scale of thermal power installed capacity and the proportion of thermal power installed capacity phased out; the number of new substations is determined by the cost of substation renovation and upgrades and the unit construction cost of substations;

[0093] The additional length of the transmission line is determined by the investment in the transformation of the transmission line and the unit cost of the transmission line reconstruction.

[0094] Furthermore, in an optional embodiment, the first model is:

[0095] Regional total installed capacity = initial value of regional total installed capacity + correction parameter for regional installed capacity change;

[0096] Change in regional installed capacity = change in thermal power installed capacity + new installed capacity of virtual power plants;

[0097] Change in thermal power installed capacity = 0.5 × newly added capacity due to regional power grid upgrades - 0.1 × thermal power installed capacity × proportion of thermal power installed capacity eliminated;

[0098] Thermal power installed capacity = initial value of thermal power installed capacity + correction parameter for changes in thermal power installed capacity;

[0099] Thermal power market share = thermal power installed capacity / regional total installed capacity;

[0100] Number of new substations = cost of substation renovation and upgrade / unit construction cost of substation;

[0101] Newly added length of transmission line = transmission line reconstruction investment / unit cost of transmission line reconstruction.

[0102] It should be noted that the changes in installed capacity and thermal power installed capacity in the above-mentioned regions are processed by the INTEG function in the system dynamics simulation software VENSIM to obtain their correction parameters.

[0103] Based on the content of the above embodiment, constructing the second model includes:

[0104] Obtaining the influencing parameter affecting the investment and operation of the regional virtual power plant as the second influencing parameter;

[0105] Establishing a correlation relationship between the second influencing parameters;

[0106] Based on the correlation between the second influencing parameters, a system dynamics flow diagram equation is constructed as the second model to characterize the investment and operation status of regional virtual power plants.

[0107] In this embodiment, the influencing parameter affecting the investment and operation of the regional virtual power plant is first obtained as the second influencing parameter, and then Figure 3 As shown in the figure, the correlation between the second influencing parameters is established, thereby obtaining the system dynamics flow diagram equation for characterizing the investment and operation status of regional virtual power plants, that is, the second model. The second model can then be used to deduce the changes in the scale diffusion, R&D investment and total cost of virtual power plants in the target area.

[0108] Based on the content of the above embodiment, the influencing parameters affecting the investment and operation of regional virtual power plants, that is, the second influencing parameters, include:

[0109] Cumulative R&D investment, cumulative installed capacity of virtual power plants, investment in new installed capacity of virtual power plants, the impact of R&D investment on distributed energy generation and energy storage systems, total cost of distributed energy generation, total cost of energy storage systems, and total cost of virtual power plants;

[0110] Among them, the total cost of the virtual power plant is determined by the total cost of distributed energy generation, the total cost of energy storage system operation and the demand response call cost; the total cost of distributed energy generation is determined by the distributed photovoltaic cost, distributed wind power cost and distributed gas power generation cost; the total cost of the energy storage system is determined by the initial cost of energy storage system operation; the demand response call cost is determined by the demand response call ratio and the demand response call price; the cumulative R&D investment is determined by the virtual power plant profit and the proportion of new R&D investment; the scale diffusion of the virtual power plant is determined by the new installed capacity investment of the virtual power plant and the investment per unit installed capacity.

[0111] Furthermore, in an optional embodiment, the second model is:

[0112] Total cost of a virtual power plant = distributed wind power cost + distributed photovoltaic cost + distributed gas power generation cost + total energy storage system cost + demand response call cost;

[0113] Distributed wind power cost = virtual power plant power generation × distributed wind power generation ratio × distributed wind power unit electricity cost × (1-the impact of R&D investment on distributed wind power generation cost);

[0114] Distributed PV cost = virtual power plant power generation × distributed PV power generation ratio × distributed PV unit electricity cost × (1-the impact of R&D investment on distributed PV power generation cost);

[0115] Distributed gas-fired power generation cost = virtual power plant power generation × distributed gas-fired power generation ratio × distributed gas-fired power generation unit electricity cost × (1-the impact of R&D investment on distributed gas-fired power generation cost);

[0116] Total cost of energy storage system = initial cost of energy storage system operation × (1-the impact of R&D investment on energy storage system operating cost);

[0117] Demand response call cost = virtual power plant power generation × demand response call ratio × demand response call price;

[0118] Cumulative R&D investment = initial R&D investment value + correction parameter for new R&D investment;

[0119] New R&D investment = virtual power plant profit × new R&D investment ratio;

[0120] Virtual power plant scale diffusion = initial value of virtual power plant installed capacity + correction parameter of virtual power plant's newly installed capacity;

[0121] New installed capacity of virtual power plants = investment in new installed capacity of virtual power plants / investment per unit installed capacity + 0.5 × regional power grid upgrade capacity.

[0122] Similarly, the correction parameters of the above parameters are also obtained through the integer function in the system dynamics simulation software vensim.

[0123] Based on the content of the above embodiment, constructing the third model includes:

[0124] Obtaining an influencing parameter affecting regional power demand as a third influencing parameter;

[0125] Establishing correlations between third influencing parameters;

[0126] Based on the correlation between the third influencing parameters, a system dynamics flow diagram equation for characterizing the regional power demand situation is constructed as the third model.

[0127] In this embodiment, the influencing parameter affecting the regional power demand is first obtained as the third influencing parameter, and then Figure 4 As shown in the figure, the correlation between the third influencing parameters is established, thereby obtaining the system dynamics flow diagram equation for characterizing the regional electricity demand situation, that is, the third model. The third model can then be used to deduce the changes in the total social electricity demand in the target area, the regional virtual power plant electricity sales volume and profits.

[0128] It is understandable that Figures 2 to 4 Appeared in <time>is the inherent equation in vensim, except <time>In addition, variables with "<>" refer to the mapping of variables that have appeared in other diagrams. Figure 2 For example, the <diffusion of virtual power plant scale> is Figure 3 Mapping of variables for the scale diffusion of virtual power plants.

[0129] Based on the content of the above embodiment, the influencing parameters affecting the regional power demand, that is, the third influencing parameters, include:

[0130] Regional GDP, regional population, regional GDP growth rate, regional population growth rate, electricity sales of virtual power plants, electricity sales of thermal power plants, regional total social electricity demand, electricity consumption of the primary industry, electricity consumption of the secondary industry, electricity consumption of the tertiary industry, regional residential electricity consumption, virtual power plant electricity sales revenue, virtual power plant profits, and virtual power plant electricity sales subsidies;

[0131] Among them, the profit of the virtual power plant is determined by electricity sales revenue, subsidies and costs; the region's total social electricity demand is determined by the electricity consumption of the primary industry, secondary industry, tertiary industry and residents; the electricity consumption of the primary industry, secondary industry and tertiary industry is determined by the output value of each industry and the electricity consumption intensity of each industry; the region's residential electricity consumption is determined by the total population of the region and per capita residential electricity consumption; the electricity sales of the virtual power plant is determined by the virtual power plant's market share and the region's total social electricity demand.

[0132] Furthermore, in an optional embodiment, the third model is:

[0133] Virtual power plant electricity sales subsidy = regional virtual power plant electricity sales × virtual power plant unit electricity sales subsidy;

[0134] Virtual power plant profit = virtual power plant electricity sales revenue + virtual power plant electricity sales subsidy - virtual power plant total cost;

[0135] Total electricity consumption in a region = electricity consumption in the primary industry + electricity consumption in the secondary industry + electricity consumption in the tertiary industry + electricity consumption in the region's residents;

[0136] Electricity consumption of primary industry = regional GDP × proportion of primary industry × electricity consumption intensity of primary industry;

[0137] Secondary industry electricity consumption = regional GDP × secondary industry share × secondary industry electricity intensity;

[0138] Electricity consumption of the tertiary industry = regional GDP × proportion of the tertiary industry × electricity intensity of the tertiary industry;

[0139] Regional GDP = initial regional GDP + (regional GDP growth rate × regional GDP) correction parameter;

[0140] Regional residential electricity consumption = regional population × per capita residential electricity consumption;

[0141] Total regional population = initial regional population + correction parameter of (regional population growth rate × total regional population);

[0142] The amount of electricity sold by a virtual power plant = the total social electricity demand in the region × the market share of the virtual power plant ÷ (1-line loss rate).

[0143] Similarly, the correction parameters of the above parameters are also obtained through the integer function in the system dynamics simulation software vensim.

[0144] Furthermore, in some possible embodiments, corresponding to the first model, the second model, and the third model, the target data respectively include:

[0145] Data representing the installed power capacity in the target region: initial value of the region's total installed capacity, initial value of thermal power installed capacity, unit cost of transmission line reconstruction, unit construction cost of substations, and the proportion of thermal power installed capacity eliminated; the proportion of thermal power installed capacity eliminated can be set for each year using a table function.

[0146] Data representing the investment and operation of virtual power plants in the target area: initial R&D investment, proportion of new R&D investment, investment amount per unit installed capacity, initial cost of energy storage system operation, internal line loss rate of the virtual power plant, initial value of the virtual power plant scale, proportion of new installed capacity investment, unit power generation cost of each distributed energy source, power generation proportion of each distributed energy source and demand response call proportion, and demand response call price; among them, the investment amount per unit installed capacity can be set as annual data using a table function.

[0147] Data representing the investment and operation of virtual power plants in the target region: initial value of regional GDP, regional GDP growth rate, initial value of regional total population, regional population growth rate, per capita residential electricity consumption, GDP share of the primary, secondary and tertiary industries, unit electricity sales subsidy for regional virtual power plants, electricity consumption intensity of various industries in the region, and regional electricity market transaction electricity prices; among them, the GDP growth rate and unit electricity sales subsidy for virtual power plants can be set for each year using table functions.

[0148] In summary, the specific operation process of the method for calculating the regional virtual power plant scale diffusion trend for power grid planning provided by the above embodiment is as follows: Figure 5 shown.

[0149] Furthermore, taking the scale diffusion of virtual power plants in a certain city as an example, the accuracy of the calculation of the scale diffusion trend of virtual power plants in the city using the method for calculating the scale diffusion trend of regional virtual power plants for power grid planning provided by the above embodiment is specifically verified.

[0150] First, the data representing the scale and spread of virtual power plants in the city, that is, the data representing the city's installed power capacity, the investment and operation of virtual power plants, and the investment and operation of virtual power plants, are shown in Tables 1, 2, and 3 respectively:

[0151] Table 1 Parameters of the system dynamics model representing the city's installed power capacity

[0152]

[0153] Table 2 Dynamic model calculation parameters representing the investment and operation of the city's virtual power plant

[0154]

[0155]

[0156] Table 3 Parameters of the dynamic model representing the city's electricity demand

[0157]

[0158] Then, based on the data in Tables 1 to 3, the initial year is set to 2023, the end year is set to 2040, and the time step is 1 year. The system dynamics simulation software Vensim is used to simulate and obtain the scale diffusion of virtual power plants in the city and the changes in the market share of virtual power plants. Figure 6 and Figure 7 As shown in the figure, the scale of virtual power plants will increase from 10.04 million kilowatts in 2023 to 26.8031 million kilowatts in 2040, and the market share of virtual power plants will increase from 12.3% in 2023 to 27.8% in 2040. It can be seen that the method provided by the embodiments of the present invention provides a reliable channel for virtual power plant operators, investors, or power grid companies to input actual data and calculate the scale and diffusion of virtual power plants according to their own needs, thereby facilitating their analysis of the economic feasibility of virtual power plant investment, construction, and operation.

[0159] The present invention divides the virtual power plant scale diffusion system into a regional power installed capacity subsystem, a virtual power plant investment and operation subsystem, and a regional power demand subsystem by fully considering the internal operating characteristics of the virtual power plant and key influencing factors such as external regional power demand and grid planning. It then establishes a dynamic model of the regional virtual power plant scale diffusion system to reveal the inherent mechanism and evolution law of the regional virtual power plant scale diffusion, analyze the economic feasibility of virtual power plant investment, construction and operation, and help the power grid accurately evaluate the impact of virtual power plant access on grid planning schemes.

[0160] An embodiment of the present invention also provides a regional virtual power plant scale diffusion calculation system. The regional virtual power plant scale diffusion calculation system described below can be considered as a module architecture for implementing the method for calculating the regional virtual power plant scale diffusion trend for power grid planning provided by an embodiment of the present invention; the content described below can be cross-referenced with the above.

[0161] Optional, see Figure 8 , Figure 8 This is a structural block diagram of a regional virtual power plant scale diffusion estimation system provided by an embodiment of the present invention. The system may include:

[0162] The acquisition unit 10 is used to obtain target data, where the target data is data representing the scale diffusion of virtual power plants in the target area.

[0163] The processing unit 20 is used to input the target data into the measurement model to obtain the results of the scale diffusion of virtual power plants in the target area. The measurement model is a system dynamics model constructed based on pre-selected influencing parameters. The system dynamics model includes: a first model for characterizing the regional installed power capacity, a second model for characterizing the investment and operation of regional virtual power plants, and a third model for characterizing the regional power demand. The influencing parameters are parameters whose impact on the scale diffusion of virtual power plants exceeds a preset influencing threshold.

[0164] Optional, see Figure 9 , Figure 9 FIG2 is a structural block diagram of another regional virtual power plant scale diffusion calculation system provided by an embodiment of the present invention. Figure 8 Based on the embodiment shown, the system further includes:

[0165] A construction unit 30 is configured to obtain an influencing parameter affecting the regional power installed capacity as a first influencing parameter;

[0166] Establishing a correlation relationship between first influencing parameters;

[0167] Based on the correlation between the first influencing parameters, a system dynamics flow diagram equation for characterizing the regional power installed capacity is constructed as the first model.

[0168] Optionally, the first influencing parameter includes:

[0169] Total regional installed capacity, change in regional installed capacity, scale of thermal power installed capacity, change in thermal power installed capacity, proportion of thermal power installed capacity phased out, thermal power market share, virtual power plant market share, capacity added from regional grid upgrades, additional length of transmission lines, investment in transmission line renovation, number of new substations, and costs of substation renovation and upgrades;

[0170] Among them, the total installed capacity of the region is determined by the initial value of the regional installed capacity, the new capacity added by the regional power grid upgrade, the new installed capacity of the virtual power plant and the change in thermal power installed capacity; the change in thermal power installed capacity is determined by the scale of thermal power installed capacity and the proportion of thermal power installed capacity elimination; the number of new substations is determined by the cost of substation transformation and upgrading and the unit construction cost of substations, and the new length of transmission lines is determined by the investment in transmission line transformation and the unit cost of transmission line reconstruction.

[0171] Optionally, the first model is:

[0172] Regional total installed capacity = initial value of regional total installed capacity + correction parameter for regional installed capacity change;

[0173] Change in regional installed capacity = change in thermal power installed capacity + new installed capacity of virtual power plants;

[0174] Change in thermal power installed capacity = 0.5 × newly added capacity due to regional power grid upgrades - 0.1 × thermal power installed capacity × proportion of thermal power installed capacity eliminated;

[0175] Thermal power installed capacity = initial value of thermal power installed capacity + correction parameter for changes in thermal power installed capacity;

[0176] Thermal power market share = thermal power installed capacity / regional total installed capacity;

[0177] Number of new substations = cost of substation renovation and upgrade / unit construction cost of substation;

[0178] Newly added length of transmission line = transmission line reconstruction investment / unit cost of transmission line reconstruction.

[0179] Optionally, the construction unit 30 is further configured to obtain an influencing parameter affecting the investment and operation of the regional virtual power plant as a second influencing parameter;

[0180] Establishing a correlation relationship between the second influencing parameters;

[0181] Based on the correlation between the second influencing parameters, a system dynamics flow diagram equation is constructed as the second model to characterize the investment and operation status of regional virtual power plants.

[0182] Optionally, the second influencing parameter includes:

[0183] Cumulative R&D investment, cumulative installed capacity of virtual power plants, investment in new installed capacity of virtual power plants, the impact of R&D investment on distributed energy generation and energy storage systems, total cost of distributed energy generation, total cost of energy storage systems, and total cost of virtual power plants;

[0184] Among them, the total cost of the virtual power plant is determined by the total cost of distributed energy generation, the total cost of energy storage system operation and the demand response call cost; the total cost of distributed energy generation is determined by the distributed photovoltaic cost, distributed wind power cost and distributed gas power generation cost; the total cost of the energy storage system is determined by the initial cost of energy storage system operation; the demand response call cost is determined by the demand response call ratio and the demand response call price; the cumulative R&D investment is determined by the virtual power plant profit and the proportion of new R&D investment; the scale diffusion of the virtual power plant is determined by the new installed capacity investment of the virtual power plant and the investment per unit installed capacity.

[0185] Optionally, the second model is:

[0186] Total cost of a virtual power plant = distributed wind power cost + distributed photovoltaic cost + distributed gas power generation cost + total energy storage system cost + demand response call cost;

[0187] Distributed wind power cost = virtual power plant power generation × distributed wind power generation ratio × distributed wind power unit electricity cost × (1-the impact of R&D investment on distributed wind power generation cost);

[0188] Distributed PV cost = virtual power plant power generation × distributed PV power generation ratio × distributed PV unit electricity cost × (1-the impact of R&D investment on distributed PV power generation cost);

[0189] Distributed gas-fired power generation cost = virtual power plant power generation × distributed gas-fired power generation ratio × distributed gas-fired power generation unit electricity cost × (1-the impact of R&D investment on distributed gas-fired power generation cost);

[0190] Total cost of energy storage system = initial cost of energy storage system operation × (1-the impact of R&D investment on energy storage system operating cost);

[0191] Demand response call cost = virtual power plant power generation × demand response call ratio × demand response call price;

[0192] Cumulative R&D investment = initial R&D investment value + correction parameter for new R&D investment;

[0193] New R&D investment = virtual power plant profit × new R&D investment ratio;

[0194] Virtual power plant scale diffusion = initial value of virtual power plant installed capacity + correction parameter of virtual power plant's newly installed capacity;

[0195] New installed capacity of virtual power plants = investment in new installed capacity of virtual power plants / investment per unit installed capacity + 0.5 × regional power grid upgrade capacity.

[0196] Optionally, the construction unit 30 is further configured to obtain an influencing parameter affecting regional power demand as a third influencing parameter;

[0197] Establishing correlations between third influencing parameters;

[0198] Based on the correlation between the third influencing parameters, a system dynamics flow diagram equation for characterizing the regional power demand situation is constructed as the third model.

[0199] Optionally, the third influencing parameter includes:

[0200] Regional GDP, regional population, regional GDP growth rate, regional population growth rate, electricity sales of virtual power plants, electricity sales of thermal power plants, regional total social electricity demand, electricity consumption of the primary industry, electricity consumption of the secondary industry, electricity consumption of the tertiary industry, regional residential electricity consumption, virtual power plant electricity sales revenue, virtual power plant profits, and virtual power plant electricity sales subsidies;

[0201] Among them, the profit of the virtual power plant is determined by electricity sales revenue, subsidies and costs; the region's total social electricity demand is determined by the electricity consumption of the primary industry, secondary industry, tertiary industry and residents; the electricity consumption of the primary industry, secondary industry and tertiary industry is determined by the output value of each industry and the electricity consumption intensity of each industry; the region's residential electricity consumption is determined by the total population of the region and per capita residential electricity consumption; the electricity sales of the virtual power plant is determined by the virtual power plant's market share and the region's total social electricity demand.

[0202] Optionally, the third model is:

[0203] Virtual power plant electricity sales subsidy = regional virtual power plant electricity sales × virtual power plant unit electricity sales subsidy;

[0204] Virtual power plant profit = virtual power plant electricity sales revenue + virtual power plant electricity sales subsidy - virtual power plant total cost;

[0205] Total electricity consumption in a region = electricity consumption in the primary industry + electricity consumption in the secondary industry + electricity consumption in the tertiary industry + electricity consumption in the region's residents;

[0206] Electricity consumption of primary industry = regional GDP × proportion of primary industry × electricity consumption intensity of primary industry;

[0207] Secondary industry electricity consumption = regional GDP × secondary industry share × secondary industry electricity intensity;

[0208] Electricity consumption of the tertiary industry = regional GDP × proportion of the tertiary industry × electricity intensity of the tertiary industry;

[0209] Regional GDP = initial regional GDP + (regional GDP growth rate × regional GDP) correction parameter;

[0210] Regional residential electricity consumption = regional population × per capita residential electricity consumption;

[0211] Total regional population = initial regional population + correction parameter of (regional population growth rate × total regional population);

[0212] The amount of electricity sold by a virtual power plant = the total social electricity demand in the region × the market share of the virtual power plant ÷ (1-line loss rate).

[0213] Below, reference Figure 10 To describe the electronic device provided by the embodiment of the present application, the electronic device provided by the embodiment may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0214] In the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 communicate with each other through the communication bus 400; obviously, Figure 10 The communication connections shown for the processor 100, communication interface 200, memory 300, and communication bus 400 are merely optional;

[0215] Optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module; the processor 100 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement an embodiment of the present invention.

[0216] The memory 300 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0217] The processor 100 is specifically configured to execute an application program in the memory to implement the steps of the above-mentioned crane hoisting wire rope installation control method.

[0218] The above embodiments are provided for illustrative purposes only and are not intended to limit the scope of implementation. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to provide an exhaustive list of all implementations. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.< / time> < / time>

Claims

1. A method for calculating the scale diffusion trend of regional virtual power plants for power grid planning, characterized by: include: Acquiring target data, wherein the target data is data representing the scale and spread of virtual power plants in a target area; Inputting the target data into the calculation model to obtain results on the scale diffusion trend of virtual power plants in the target area; The calculation model is a system dynamics model constructed based on preselected influencing parameters, and the system dynamics model includes: A first model for characterizing regional power installed capacity, a second model for characterizing regional virtual power plant investment and operation, and a third model for characterizing regional power demand; The impact parameter is a parameter whose impact on the scale diffusion of virtual power plants exceeds a preset impact threshold.

2. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 1 is characterized in that: Constructing the first model includes: Obtaining the influencing parameter affecting the installed power capacity in the region as a first influencing parameter; establishing an association relationship between the first influencing parameters; Based on the correlation between the first influencing parameters, a system dynamics flow diagram equation for characterizing the installed power capacity in the region is constructed as the first model.

3. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 2 is characterized in that: The first influencing parameters include: Total regional installed capacity, change in regional installed capacity, scale of thermal power installed capacity, change in thermal power installed capacity, proportion of thermal power installed capacity phased out, thermal power market share, virtual power plant market share, capacity added from regional grid upgrades, additional length of transmission lines, investment in transmission line renovation, number of new substations, and costs of substation renovation and upgrades; The total installed capacity of the region is determined by the initial value of the regional installed capacity, the newly added capacity from the regional power grid upgrade, the newly added installed capacity of the virtual power plant, and the change in the thermal power installed capacity; The change in thermal power installed capacity is determined by the scale of thermal power installed capacity and the proportion of thermal power installed capacity that is phased out; The number of new substations is determined by the cost of transformation and upgrading of the substations and the unit construction cost of the substations; The additional length of the transmission line is determined by the investment in the transformation of the transmission line and the unit cost of the transmission line reconstruction.

4. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 3 is characterized in that: The first model is: Regional total installed capacity = initial value of regional total installed capacity + correction parameter for regional installed capacity change; Change in regional installed capacity = change in thermal power installed capacity + new installed capacity of virtual power plants; Change in thermal power installed capacity = 0.5 × newly added capacity due to regional power grid upgrades - 0.1 × thermal power installed capacity × proportion of thermal power installed capacity eliminated; Thermal power installed capacity = initial value of thermal power installed capacity + correction parameter for changes in thermal power installed capacity; Thermal power market share = thermal power installed capacity / regional total installed capacity; Number of new substations = cost of substation renovation and upgrade / unit construction cost of substation; Newly added length of transmission line = transmission line reconstruction investment / unit cost of transmission line reconstruction.

5. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 1 is characterized in that: Constructing the second model includes: Obtaining the influencing parameter affecting the investment and operation of the virtual power plant in the region as a second influencing parameter; establishing an association relationship between the second influencing parameters; Based on the correlation between the second influencing parameters, a system dynamics flow diagram equation for characterizing the investment and operation status of the virtual power plant in the region is constructed as the second model.

6. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 5 is characterized in that: The second influencing parameters include: Cumulative R&D investment, cumulative installed capacity of virtual power plants, investment in new installed capacity of virtual power plants, the impact of R&D investment on distributed energy generation and energy storage systems, total cost of distributed energy generation, total cost of energy storage systems, and total cost of virtual power plants; The total cost of the virtual power plant is determined by the total cost of distributed energy generation, the total cost of energy storage system operation, and the cost of demand response. The total cost of distributed energy generation is determined by the cost of distributed photovoltaic power generation, distributed wind power generation and distributed gas power generation; The total cost of the energy storage system is determined by the initial cost of operating the energy storage system; The demand response call cost is determined by the demand response call ratio and the demand response call price; The cumulative amount of R&D investment is determined by the profit of the virtual power plant and the proportion of new R&D investment; The scale diffusion of virtual power plants is determined by the investment in new installed capacity of virtual power plants and the investment per unit installed capacity.

7. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 6 is characterized in that: The second model is: Total cost of a virtual power plant = distributed wind power cost + distributed photovoltaic cost + distributed gas power generation cost + total energy storage system cost + demand response call cost; Distributed wind power cost = virtual power plant power generation × distributed wind power generation ratio × distributed wind power unit electricity cost × (1-the impact of R&D investment on distributed wind power generation cost); Distributed PV cost = virtual power plant power generation × distributed PV power generation ratio × distributed PV unit electricity cost × (1-the impact of R&D investment on distributed PV power generation cost); Distributed gas-fired power generation cost = virtual power plant power generation × distributed gas-fired power generation ratio × distributed gas-fired power generation unit electricity cost × (1-the impact of R&D investment on distributed gas-fired power generation cost); Total cost of energy storage system = initial cost of energy storage system operation × (1-the impact of R&D investment on energy storage system operating cost); Demand response call cost = virtual power plant power generation × demand response call ratio × demand response call price; Cumulative R&D investment = initial R&D investment value + correction parameter for new R&D investment; New R&D investment = virtual power plant profit × new R&D investment ratio; Virtual power plant scale diffusion = initial value of virtual power plant installed capacity + correction parameter of virtual power plant's newly installed capacity; New installed capacity of virtual power plants = investment in new installed capacity of virtual power plants / investment per unit installed capacity + 0.5 × regional power grid upgrade capacity.

8. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 1 is characterized in that: Constructing the third model includes: Obtaining the influencing parameter affecting the power demand in the region as a third influencing parameter; Establishing an association relationship between the third influencing parameters; Based on the correlation between the third influencing parameters, a system dynamics flow diagram equation for characterizing the regional power demand situation is constructed as the third model.

9. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 8, characterized in that: The third influencing parameter includes: Regional GDP, regional population, regional GDP growth rate, regional population growth rate, electricity sales of virtual power plants, electricity sales of thermal power plants, regional total social electricity demand, electricity consumption of the primary industry, electricity consumption of the secondary industry, electricity consumption of the tertiary industry, regional residential electricity consumption, virtual power plant electricity sales revenue, virtual power plant profits, and virtual power plant electricity sales subsidies; The profit of the virtual power plant is determined by electricity sales revenue, subsidies and costs; The total electricity demand of the region is determined by the electricity consumption of the first industry, the electricity consumption of the second industry, the electricity consumption of the tertiary industry and the electricity consumption of residents; The electricity consumption of the first industry, the electricity consumption of the second industry and the electricity consumption of the tertiary industry are determined by the output value of each industry and the electricity consumption intensity of each industry; The residential electricity consumption in the said area is determined by the total population of the said area and the per capita residential electricity consumption; The amount of electricity sold by the virtual power plant is determined by the virtual power plant's market share and the region's overall social electricity demand.

10. The method for calculating the scale diffusion trend of regional virtual power plants for power grid planning according to claim 9, characterized in that: The third model is: Virtual power plant electricity sales subsidy = regional virtual power plant electricity sales × virtual power plant unit electricity sales subsidy; Virtual power plant profit = virtual power plant electricity sales revenue + virtual power plant electricity sales subsidy - virtual power plant total cost; Total electricity consumption in a region = electricity consumption in the primary industry + electricity consumption in the secondary industry + electricity consumption in the tertiary industry + electricity consumption in the region's residents; Electricity consumption of primary industry = regional GDP × proportion of primary industry × electricity consumption intensity of primary industry; Secondary industry electricity consumption = regional GDP × secondary industry share × secondary industry electricity intensity; Electricity consumption of the tertiary industry = regional GDP × proportion of the tertiary industry × electricity intensity of the tertiary industry; Regional GDP = initial regional GDP + (regional GDP growth rate × regional GDP) correction parameter; Regional residential electricity consumption = regional population × per capita residential electricity consumption; Total regional population = initial regional population + correction parameter of (regional population growth rate × total regional population); virtual power plant electricity sales = total regional social electricity demand × virtual power plant market share ÷ (1-line loss rate).

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