Country distributed energy system development path prediction method based on system dynamics

Abstract

The invention relates to a rural distributed energy system development path prediction method based on system dynamics, and belongs to the technical field of rural energy system computation.The rural distributed energy system development path prediction method comprises the steps that an evolution element system covering sources, networks and loads is constructed, and composition and internal association of index elements, non-index elements and process elements are clarified; on this basis, causal association analysis is carried out, a renewable energy power generation sub-module, a net rack consolidation sub-module and a load energy consumption sub-module are constructed, and a coupling relationship among the modules is determined; and further, establishing a system dynamics model based on the visual flow graph, inputting an associated element parameter equation, and realizing dynamic simulation and prediction of the rural distributed energy system development path. According to the method, multi-dimensional factors such as policies, markets and technologies can be fused, dependence on historical data is avoided, and an effective tool is provided for scientific planning and policy making of a rural distributed energy system.

Description

Technical Field

[0001] This invention belongs to the field of rural energy system prediction technology, and in particular relates to a method for predicting the development path of rural distributed energy systems based on system dynamics. Background Technology

[0002] Against the backdrop of rural revitalization and energy transition, the planning and development path prediction of rural distributed energy systems have become critical issues. However, historical data on rural energy systems is scarce and the interrelationships of factors are complex, making it difficult for traditional methods to effectively predict their evolutionary trends. Therefore, there is an urgent need for a prediction method that does not rely on historical data, considers the interaction of various factors such as policies and markets, and dynamically simulates the development path of rural distributed energy systems.

[0003] System dynamics methods can handle multivariable, nonlinear dynamic systems and are widely used in evolutionary path analysis in economics and markets. In the power system field, this method is often used to analyze the structural evolution of the power market and predict the future prospects of related power technologies. A small number of studies have also applied it to the development path analysis of urban power system power supply structures. However, the application of this method in rural distributed energy systems is still immature, lacking comprehensive causal relationship analysis and systematic modeling of the factors influencing the evolution of power sources, grids, and loads. Summary of the Invention

[0004] The technical problem this invention aims to solve is the scarcity of historical data, complex factor relationships, and the inapplicability of traditional prediction methods in predicting the development path of rural distributed energy systems. It proposes a system dynamics-based method for predicting the development path of rural distributed energy systems. First, by constructing an evolutionary element system encompassing sources, grids, and loads, the composition and intrinsic relationships of indicator elements, non-indicator elements, and process elements are clarified. Based on this, causal correlation analysis is conducted to construct a renewable energy generation module, a grid consolidation sub-module, and a load energy consumption sub-module, and the coupling relationships between modules are determined. Further, a system dynamics model is established based on a visualized flow graph, and the parametric equations of the related elements are input to achieve dynamic simulation and prediction of the development path of rural distributed energy systems. This method can integrate multi-dimensional factors such as policy, market, and technology, avoid dependence on historical data, and provide an effective tool for the scientific planning and policy formulation of rural distributed energy systems. Attached Figure Description

[0005] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a causal relationship analysis diagram of the source-grid-load elements of a rural distributed energy system; Figure 3 It is a visualization flowchart of the system evolution path analysis model; Figure 4This is the source-side development prediction result of the embodiment; Figure 5 This is the network-side development prediction result of the embodiment; Figure 6 This is the load-side development prediction result of the embodiment. Detailed Implementation

[0006] The following detailed description of the method for predicting the development path of rural distributed energy systems based on system dynamics, proposed in this invention, is provided in conjunction with embodiments and accompanying drawings.

[0007] This invention provides a method for predicting the development path of rural distributed energy systems based on system dynamics, such as... Figure 1 As shown, it includes the following steps: 1) Based on the established source-grid-load evolution element system of the rural distributed energy system, input three categories of evolution elements: indicator elements, non-indicator elements, and process elements; input the simulation start time as... The simulation stopping time is The simulation step size is Set the current time ; (1) The source-grid-load evolution element system of the rural distributed energy system can be represented as:

[0008] In the formula, For indicator element set, For non-indicator element set, For process element set, , , The evolutionary elements contained therein can be represented as follows:

[0009] In the formula, Containing elements For total installed photovoltaic capacity, For the total installed capacity of wind power, For the total installed capacity of biomass, For the total installed capacity of hydropower, For the total installed capacity of geothermal power generation, For the total installed capacity demand of distributed energy, For voltage qualification rate, Average annual power outage time per household Electricity for rural residents Electricity for rural tourism For electricity for public infrastructure Electricity for rural industries Electricity for agricultural irrigation and drainage Electricity for agricultural production Total annual electricity consumption;

[0010] In the formula, Containing elements For the growth rate of centralized photovoltaic infrastructure, As a photovoltaic policy incentive factor, For the potential of photovoltaic development, For the growth of distributed photovoltaic power, For government planning of photovoltaic projects, To encourage farmers to invest in photovoltaic power, For wind power base growth rate, As a wind power policy incentive factor, For the potential of wind power development, For government planning of wind power projects, For the growth rate of biomass base, As a policy incentive factor for biomass, For the exploitable potential of biomass, For government planning of biomass projects, For hydropower infrastructure growth rate, Incentive factors for hydropower policies, For the potential of hydropower development, For the basic growth rate of geothermal power generation, As a policy incentive factor for geothermal power generation, Geothermal power generation potential For the ratio of new lines, For ring network rate, For the annual planned installed capacity of flexible interconnect devices, For the total installed capacity of flexible interconnect devices, For net population growth rate, For population size, For the growth rate of electricity consumption in rural tourism, For the proportion of electricity used by public infrastructure, For the growth rate of electricity consumption in rural industries, For government planning of industrial park load, Electricity for agricultural machinery For agricultural output growth rate, For the growth rate of electric agricultural machinery, To meet the demand for renewable energy consumption, To meet the electricity demand of the load;

[0011] In the formula, Containing elements For photovoltaic growth rate, For the growth of photovoltaic power, For wind power growth rate, For the growth of wind power, For biomass growth rate, For biomass growth, For hydropower growth rate, For the increase in hydropower, For the growth rate of geothermal power generation, For the increase in geothermal power generation, To enhance the effect factor of flexible interconnection, To improve the quality of energy supply To meet the demand for improved energy quality, For voltage qualification rate growth rate, Factors to improve the efficiency of power supply quality for newly added substations For the increase in voltage qualification rate, For the newly added power supply quality improvement effect factor, The effect factor for improving the reliability of power supply in ring network environments, The average annual power outage time reduction rate per household, To reduce the average annual power outage time per household For the average daily power consumption of rural residents, For the annual increase in electricity consumption of rural residents, To increase electricity consumption for rural tourism For the increase in electricity consumption of public infrastructure, For the increase in electricity consumption in rural industries, Increased electricity consumption for agricultural irrigation and drainage Increased electricity consumption for agricultural production Increased electricity consumption for electric agricultural machinery.

[0012] 2) Based on the source-grid-load evolution elements of the rural distributed energy system input in step 1), perform causal correlation analysis to construct a renewable energy generation submodule, a grid consolidation submodule, and a load energy consumption submodule; the causal correlation analysis results of the renewable energy generation submodule, the grid consolidation submodule, and the load energy consumption submodule can be represented by an adjacency matrix; the definition rules of the adjacency matrix are as follows: Adjacency matrix The rows and columns of the adjacency matrix represent all evolutionary elements contained in the same module arranged in the same order. The diagonal elements of the adjacency matrix are all 0, and the values ​​of the remaining elements are defined as follows:

[0013] If elements With elements There is a causal relationship, and the elements As cause, element As a result, then we have , ; if element With elements If there is no causal relationship, then there is Obviously, the adjacency matrix is ​​a highly sparse square matrix, so it is only necessary to give the order of the evolutionary elements corresponding to the rows and columns and the elements that are 1 in the matrix.

[0014] 3) Based on the renewable energy generation module, grid reinforcement submodule, and load energy consumption submodule in step 2), determine the coupling relationships between the submodules and draw a visual flow diagram of the rural distributed energy system; the module coupling relationship refers to:

[0015]

[0016] In the formula, To meet the demand for renewable energy consumption, To meet the total installed capacity demand of distributed energy resources, To meet the electricity demand of the load, This represents the total annual electricity consumption.

[0017] 4) Based on the visualized flow chart of the rural distributed energy system drawn in step 3), input the related element parameter equations to establish a prediction model for the development path of the rural distributed energy system based on system dynamics; all related element parameter equations include: (1) The related parameter equations for the renewable energy generation module are as follows:

[0018]

[0019]

[0020]

[0021] In the formula, For total installed photovoltaic capacity, For the total installed capacity of wind power, For the total installed capacity of biomass, For the total installed capacity of hydropower, For the total installed capacity of geothermal power generation, For the total installed capacity demand of distributed energy, For the growth rate of centralized photovoltaic infrastructure, As a photovoltaic policy incentive factor, For the potential of photovoltaic development, For the growth of distributed photovoltaic power, For government planning of photovoltaic projects, To encourage farmers to invest in photovoltaic power, For wind power base growth rate, As a wind power policy incentive factor, For the potential of wind power development, For government planning of wind power projects, For the growth rate of biomass base, As a policy incentive factor for biomass, For the exploitable potential of biomass, For government planning of biomass projects, For hydropower infrastructure growth rate, Incentive factors for hydropower policies, For the potential of hydropower development, For the basic growth rate of geothermal power generation, As a policy incentive factor for geothermal power generation, Geothermal power generation potential For photovoltaic growth rate, For the growth of photovoltaic power, For wind power growth rate, For the growth of wind power, For biomass growth rate, For biomass growth, For hydropower growth rate, For the increase in hydropower, For the growth rate of geothermal power generation, This represents an increase in geothermal power generation. (2) The parametric equations of the related elements of the space frame reinforcement submodule are as follows:

[0022]

[0023]

[0024]

[0025]

[0026] In the formula, For the ratio of new lines, For ring network rate, For the total installed capacity of flexible interconnect devices, For the annual planned installed capacity of flexible interconnect devices, For the newly added power supply quality improvement effect factor, The effect factor for improving the reliability of power supply in ring network environments, To enhance the effect factor of flexible interconnection, Factors to improve the efficiency of power supply quality for newly added substations To meet the demand for renewable energy consumption, For the electricity demand of the load, For voltage qualification rate, To meet the demand for improved energy quality, Average annual power outage time per household For voltage qualification rate growth rate, The average annual power outage time reduction rate per household, To improve the quality of energy supply; (3) The related element parameter equations of the load energy consumption submodule are as follows:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, , , , , , , , , These respectively represent electricity consumption for rural residents, rural tourism, public infrastructure, rural industries, agricultural irrigation and drainage, agricultural production, total annual electricity consumption, load of government-planned industrial parks, and electricity consumption for electric agricultural machinery. , , , , These are the net population growth rate, the growth rate of electricity consumption in rural tourism, the growth rate of electricity consumption in rural industries, the growth rate of agricultural output value, and the growth rate of electric agricultural machinery. For population size, , These are respectively the average daily electricity consumption per capita of rural residents and the proportion of electricity consumption for public infrastructure. , , , , , , These figures represent the annual increase in electricity consumption for rural residents, rural tourism, public infrastructure, rural industries, agricultural irrigation and drainage, agricultural production, and electric agricultural machinery, respectively.

[0035] 5) Solve the prediction model for the development path of the rural distributed energy system based on system dynamics established in step 4), and output the evolution data of the indicator elements at the current moment; 6) Update ,judge Is it less than If yes, repeat step 5; otherwise, end the simulation process.

[0036] By following the steps above, the development path prediction function of rural distributed energy systems can be realized.

[0037] The above method is applied in the following example using a rural distributed energy system.

[0038] The input parameter data for the example are shown in Table 1. This table lists all the initial parameters required for the simulation of the development path prediction model for rural distributed energy systems, covering the three dimensions of source, grid, and load. Specifically, it includes the initial installed capacity, development potential, and policy factors of five types of distributed energy, such as photovoltaic, wind power, and biomass; the initial voltage qualification rate, outage time, and grid construction indicators on the grid side; and the socio-economic parameters on the load side, such as the initial electricity consumption of various industries, population size, and growth rate.

[0039] Table 2 shows the source-side data output from the model simulation. This table lists the key outputs of the rural distributed energy system's energy supply side during the forecast period (2025-2035), covering the total installed capacity and total system demand for five types of distributed energy: photovoltaic, wind power, and biomass. Specifically, the total installed capacity of photovoltaic power increased from 11060.00kW to 39661.84kW, wind power from 30006.00kW to 37328.89kW, and biomass from 6660.00kW to 18576.08kW. Driven by this, the total installed capacity demand for distributed energy continued to grow from 47726.00kW to 95566.80kW.

[0040] Table 3 shows the grid-side data output from the model simulation. This table compares the evolution of grid system operation quality under two scenarios: "without deployment" and "without deployment" of flexible interconnection devices. It covers two key performance indicators: voltage qualification rate and average annual outage time per household. Specifically, deploying flexible interconnection devices enables the voltage qualification rate to reach 100% more quickly, while significantly reducing the average annual outage time per household from 160 minutes to 6.979 minutes, far superior to the 22.832 minutes in the no-deployment scenario. This provides a direct comparison for evaluating the effectiveness of different grid construction strategies.

[0041] Table 4 shows the load-side data output from the model simulation. This table details the evolution of electricity consumption by various categories within the rural distributed energy system during the forecast period (2025-2035), covering six load categories: rural residents, tourism, industry, public infrastructure, agricultural production, and irrigation / drainage. Specifically, rural industrial electricity consumption surged from 26.4739 million kWh to 270.9523 million kWh, representing the main growth driver; total annual electricity consumption increased significantly from 63.9464 million kWh to 345.6564 million kWh, clearly reflecting the shift in rural load structure and the growth trend in total demand.

[0042] Causal correlation analysis diagram of source-grid-load elements in rural distributed energy system as shown in the figure. Figure 2 As shown in the figure, this diagram illustrates the causal relationships among the evolutionary elements of a rural distributed energy system. The arrows in the diagram represent the causal relationships between the evolutionary elements. If the arrow points from a to b, it means that a is the cause and b is the result.

[0043] Visual flowchart of rural distributed energy system as follows Figure 3 As shown in the figure, this diagram is a visual flow chart of the development path prediction model of rural distributed energy system based on system dynamics. The model sets up the coupling relationship between the parametric equations of related elements and the sub-modules.

[0044] The source-side development prediction results of the embodiment are as follows: Figure 4 As shown in the figure, this diagram illustrates the dynamic evolution of the source-side output data listed in Table 2, covering the total installed capacity of distributed energy sources such as photovoltaics, wind power, and biomass, as well as the total system installed capacity demand. The diagram clearly presents the dominant position of photovoltaic development and the overall trend of steady growth in the installed capacity of various energy sources over time (2025-2035), intuitively reflecting the development path of the energy supply structure.

[0045] The example compares and analyzes the network-side development prediction results under two scenarios: deployment of flexible interconnect devices and no deployment of flexible interconnect devices. Figure 5As shown in the figure, the graph presents the evolution differences of the grid-side output data listed in Table 3 under the two scenarios in the form of a comparison curve. It covers two key performance indicators: voltage qualification rate and average annual outage time per household. It reveals that the deployment of flexible interconnection devices can significantly accelerate the improvement of voltage qualification rate and greatly reduce outage time.

[0046] The load-side development prediction results of the embodiment are as follows: Figure 6 As shown in the figure, this diagram illustrates the evolution of the load-side output data listed in Table 4, covering the electricity consumption of various load categories such as rural residents, tourism, and industry. The diagram highlights the absolute dominance of rural industrial electricity consumption as the core driving force in the growth of total electricity consumption, as well as the dynamic changes in the composition of various loads over time, clearly depicting the typical characteristics of rural load development.

[0047] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

[0048] Table 1 Input data for the example

[0049] Table 2. Source-side output data for the example

[0050] Table 3. Output data from the network side of the simulation example

[0051] Table 4 Load-side output data of the example

Claims

1. A method for predicting the development path of rural distributed energy systems based on system dynamics, characterized in that... Includes the following steps: Step 1: Based on the established source-grid-load evolution element system of the rural distributed energy system, input three types of evolution elements: indicator elements, non-indicator elements, and process elements; input the simulation start time. The simulation stopping time is The simulation step size is Set the current time ; Step 2: Based on the source-grid-load evolution elements of the rural distributed energy system input in Step 1, conduct causal correlation analysis and construct a renewable energy generation module, a grid consolidation submodule, and a load energy consumption submodule. Step 3: Based on the renewable energy generation module, grid reinforcement submodule, and load energy consumption submodule in Step 2, determine the coupling relationship between the submodules and draw a visual flow diagram of the rural distributed energy system; Step 4: Based on the visualized flow chart of the rural distributed energy system drawn in Step 3, input the related element parameter equations and establish a prediction model for the development path of the rural distributed energy system based on system dynamics. Step 5: Solve the rural distributed energy system development path prediction model established in Step 4 based on system dynamics, and output the evolution data of the indicator elements at the current moment. Step 6, Update ,judge Is it less than If yes, repeat step 5; otherwise, end the simulation process.

2. The method for predicting the development path of rural distributed energy systems based on system dynamics as described in claim 1, characterized in that, The source-grid-load evolution element system of the rural distributed energy system described in step 1 can be represented as: ； In the formula, For indicator element set, For non-indicator element set, For process element set, , , The evolutionary elements contained therein can be represented as follows: ； In the formula, Containing elements For total installed photovoltaic capacity, For the total installed capacity of wind power, For the total installed capacity of biomass, For the total installed capacity of hydropower, For the total installed capacity of geothermal power generation, For the total installed capacity demand of distributed energy, For voltage qualification rate, Average annual power outage time per household Electricity for rural residents Electricity for rural tourism For electricity for public infrastructure Electricity for rural industries Electricity for agricultural irrigation and drainage Electricity for agricultural production Total annual electricity consumption; ； In the formula, Containing elements For the growth rate of centralized photovoltaic infrastructure, As a photovoltaic policy incentive factor, For the potential of photovoltaic development, For the growth of distributed photovoltaic power, For government planning of photovoltaic projects, To encourage farmers to invest in photovoltaic power, For wind power base growth rate, As a wind power policy incentive factor, For the potential of wind power development, For government planning of wind power projects, For the growth rate of biomass base, As a policy incentive factor for biomass, For the exploitable potential of biomass, For government planning of biomass projects, For hydropower infrastructure growth rate, Incentive factors for hydropower policies, For the potential of hydropower development, For the basic growth rate of geothermal power generation, As a policy incentive factor for geothermal power generation, Geothermal power generation potential For the ratio of new lines, For ring network rate, For the annual planned installed capacity of flexible interconnect devices, For the total installed capacity of flexible interconnect devices, For net population growth rate, For population size, For the growth rate of electricity consumption in rural tourism, For the proportion of electricity used by public infrastructure, For the growth rate of electricity consumption in rural industries, For government planning of industrial park load, Electricity for agricultural machinery For agricultural output growth rate, For the growth rate of electric agricultural machinery, To meet the demand for renewable energy consumption, To meet the electricity demand of the load; ； In the formula, Containing elements For photovoltaic growth rate, For the growth of photovoltaic power, For wind power growth rate, For the growth of wind power, For biomass growth rate, For biomass growth, For hydropower growth rate, For the increase in hydropower, For the growth rate of geothermal power generation, For the increase in geothermal power generation, To enhance the effect factor of flexible interconnection, To improve the quality of energy supply To meet the demand for improved energy quality, For voltage qualification rate growth rate, Factors to improve the efficiency of power supply quality for newly added substations For the increase in voltage qualification rate, For the newly added power supply quality improvement effect factor, The effect factor for improving the reliability of power supply in ring network environments, The average annual power outage time reduction rate per household, To reduce the average annual power outage time per household For the average daily power consumption of rural residents, For the annual increase in electricity consumption of rural residents, To increase electricity consumption for rural tourism For the increase in electricity consumption of public infrastructure, For the increase in electricity consumption in rural industries, Increased electricity consumption for agricultural irrigation and drainage Increased electricity consumption for agricultural production Increased electricity consumption for electric agricultural machinery.

3. The method for predicting the development path of rural distributed energy systems based on system dynamics as described in claim 1, characterized in that, The causal correlation analysis results of the renewable energy generation module, grid reinforcement submodule, and load energy consumption submodule in step 2 can be represented by an adjacency matrix; the definition rules of the adjacency matrix are as follows: Adjacency matrix The rows and columns of the adjacency matrix represent all evolutionary elements contained in the same module arranged in the same order. The diagonal elements of the adjacency matrix are all 0, and the values ​​of the remaining elements are defined as follows: ； If elements With elements There is a causal relationship, and the elements As cause, element As a result, then we have , ; If elements With elements If there is no causal relationship, then there is Obviously, the adjacency matrix is ​​a highly sparse square matrix, so it is only necessary to give the order of the evolutionary elements corresponding to the rows and columns and the elements that are 1 in the matrix.

4. The method for predicting the development path of rural distributed energy systems based on system dynamics as described in claim 1, characterized in that, The module coupling relationship in step 3 refers to: ； ； In the formula, To meet the demand for renewable energy consumption, To meet the total installed capacity demand of distributed energy resources, To meet the electricity demand of the load, This represents the total annual electricity consumption.

5. The method for predicting the development path of rural distributed energy systems based on system dynamics as described in claim 1, characterized in that, The correlation element parameter equations in step 4 include: the correlation element parameter equations for the renewable energy power generation module are: ； ； ； ； In the formula, For total installed photovoltaic capacity, For the total installed capacity of wind power, For the total installed capacity of biomass, For the total installed capacity of hydropower, For the total installed capacity of geothermal power generation, For the total installed capacity demand of distributed energy, For the growth rate of centralized photovoltaic infrastructure, As a photovoltaic policy incentive factor, For the potential of photovoltaic development, For the growth of distributed photovoltaic power, For government planning of photovoltaic projects, To encourage farmers to invest in photovoltaic power, For wind power base growth rate, As a wind power policy incentive factor, For the potential of wind power development, For government planning of wind power projects, For the growth rate of biomass base, As a policy incentive factor for biomass, For the exploitable potential of biomass, For government planning of biomass projects, For hydropower infrastructure growth rate, Incentive factors for hydropower policies, For the potential of hydropower development, For the basic growth rate of geothermal power generation, As a policy incentive factor for geothermal power generation, Geothermal power generation potential For photovoltaic growth rate, For the growth of photovoltaic power, For wind power growth rate, For the growth of wind power, For biomass growth rate, For biomass growth, For hydropower growth rate, For the increase in hydropower, For the growth rate of geothermal power generation, This represents an increase in geothermal power generation. The related element parameter equations for the space frame reinforcement submodule are as follows: ； ； ； ； ； In the formula, For the ratio of new lines, For ring network rate, For the total installed capacity of flexible interconnect devices, For the annual planned installed capacity of flexible interconnect devices, For the newly added power supply quality improvement effect factor, The effect factor for improving the reliability of power supply in ring network environments, To enhance the effect factor of flexible interconnection, Factors to improve the efficiency of power supply quality for newly added substations To meet the demand for renewable energy consumption, For the electricity demand of the load, For voltage qualification rate, To meet the demand for improved energy quality, Average annual power outage time per household For voltage qualification rate growth rate, The average annual power outage time reduction rate per household, To improve the quality of energy supply; The related parameter equations for the load energy consumption submodule are as follows: ； ； ； ； ； ； ； ； In the formula, , , , , , , , , These respectively represent electricity consumption for rural residents, rural tourism, public infrastructure, rural industries, agricultural irrigation and drainage, agricultural production, total annual electricity consumption, load of government-planned industrial parks, and electricity consumption for electric agricultural machinery. , , , , These are the net population growth rate, the growth rate of electricity consumption in rural tourism, the growth rate of electricity consumption in rural industries, the growth rate of agricultural output value, and the growth rate of electric agricultural machinery. For population size, , These are respectively the average daily electricity consumption per capita of rural residents and the proportion of electricity consumption for public infrastructure. , , , , , , These figures represent the annual increase in electricity consumption for rural residents, rural tourism, public infrastructure, rural industries, agricultural irrigation and drainage, agricultural production, and electric agricultural machinery, respectively.

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