Power system optimal configuration method for promoting consumption of rural renewable energy

Abstract

The invention relates to a power system optimal configuration method for promoting rural renewable energy consumption. The method comprises the steps of S1, constructing a county new energy power generation resource potential evaluation model; s2, setting constraint conditions such as wind power, photovoltaic power, biomass power generation output, power system operation and energy storage configuration; s3, setting a power system optimization configuration objective function; s4, performing solution sorting on the model by adopting a sorting method approaching an ideal solution; according to the invention, key elements of the power system can be optimally configured, the renewable energy consumption capability is improved, and economic and environment-friendly operation of the power system is ensured; the method has the advantages that the output volatility is considered, key elements are optimally configured on the basis of time sequence production simulation, and the absorption capacity is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to an electric power system optimization configuration method for promoting the consumption of renewable energy in rural areas. Background Art

[0002] Faced with increasingly severe energy and environmental problems around the world, the development and utilization of renewable energy to replace traditional fossil energy has become an inevitable choice to ensure energy security, respond to climate change, and achieve sustainable development. Determined by natural properties, the power generation of renewable energy represented by wind power and photovoltaic power generation has significant randomness and volatility. When renewable energy is connected to the distribution network on a large scale, the output on the power supply side will show strong uncertainty and uncontrollability; on the load side, with the increase in the penetration rate of distributed renewable energy, the equivalent load composed of power load and distributed power source will also show strong uncertainty. The uncertain characteristics of both the source and load sides increase the difficulty of balancing the real-time supply and demand of electric power; therefore, when large-scale distributed renewable energy is connected to the power system, how to adapt to the uncertainty of the instantaneous output of renewable energy and promote the consumption of renewable energy will become the primary problem to be solved in the future power system optimization operation, which has great practical significance for energy transformation; therefore, it is very necessary to provide a power system optimization configuration method that considers output volatility, optimizes the configuration of key elements based on time-series production simulation, and improves the consumption capacity to promote the consumption of rural renewable energy. Summary of the invention

[0003] 1. Technical issues

[0004] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0005] 1. When renewable energy is connected to the distribution network on a large scale, the output of the power supply side will be highly uncertain and uncontrollable;

[0006] 2. As the penetration rate of distributed renewable energy increases, the equivalent load composed of power load and distributed power sources will also show strong uncertainty. The uncertainty characteristics of both the source and the load increase the difficulty of balancing the real-time supply and demand of power;

[0007] 3. When large-scale distributed renewable energy is connected to the power system, how to adapt to the uncertainty of the instantaneous output of renewable energy and promote the consumption of renewable energy will become the primary problem to be solved in the future power system optimization operation;

[0008] 4. Research on the optimal configuration of key elements of the power system to promote the consumption of renewable energy is still not in-depth enough.

[0009] (II) Technical solution

[0010] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for optimizing the configuration of an electric power system that takes output volatility into consideration, optimizes the configuration of key elements based on time-series production simulation, and improves the absorption capacity to promote the absorption of renewable energy in rural areas.

[0011] The object of the present invention is achieved by: a method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas, the method comprising the following steps:

[0012] Step 1: Construct a model for evaluating the potential of new energy power generation resources in the county;

[0013] Step 2: Set constraints such as wind power, photovoltaic, biomass power generation output, power system operation, and energy storage configuration;

[0014] Step 3: Set the objective function of power system optimization configuration;

[0015] Step 4: Use the sorting method that approximates the ideal solution to sort the model.

[0016] Furthermore, the construction of a county-level new energy power generation resource potential assessment model in step 1 includes wind power, photovoltaic, and biomass renewable power generation resource potential assessments.

[0017] Furthermore, the wind power generation potential assessment model in step 1 is specifically: the calculation formula of effective wind energy density is: Where W is the effective wind energy density between the startup wind speed and the shutdown wind speed; ρ is the air density; V1 is the startup wind speed; V2 is the shutdown wind speed; ρ(V) is the conditional probability distribution density function within the effective wind speed range.

[0018] Furthermore, the photovoltaic power generation potential assessment model in step 1 is specifically: expressed by the ratio of the maximum value to the minimum value of the number of days with sunshine hours > 6h in each month: Where K is the solar energy resource stability index; D1, D2, ..., D 12 is the number of days with sunshine hours > 6h in each month from January to December; max and min are standard functions for finding the maximum and minimum values ​​respectively.

[0019] Furthermore, the biomass power generation potential assessment model in step 1 is specifically as follows: Total amount of crop straw: Where, CR is the theoretical reserve of crop straw; Qc i is the yield of the i-th crop; p i is the yield per unit area of ​​the i-th type of crop; q i is the planting area of ​​the i-th type of crops; r i is the cereal-to-straw ratio coefficient of the i-th category; the maximum power generation potential of agricultural biomass resources is calculated as follows: ABPP max is the maximum power generation of agricultural biomass resources; K is the overall energy conversion efficiency; T is the annual continuous operation time.

[0020] Furthermore, the constraint condition in step 2 is specifically: ∑P pv (j,t)+∑P wp (j,t)+∑P mg (j,t)+P lc (t)+∑P d (j,t)=∑P c (j,t)+P l (t)(9), P mgmin (j)≤P mg (j,t)≤P mgmax (j)(10), Where P pv (j, t) is the actual output of the j-th PV power station at the t-th moment; P wp (j,t) is the actual output of the j-th wind farm at the t-th moment; P mg (j,t) is the output of the jth biomass generator at the tth moment; P d (j,t) is the discharge power of the jth energy storage power station at time t; P lc (t) is the load shedding power of the system at time t; P c (j,t) is the charging power of the jth energy storage station at time t; P1(t) is the load power at time t; P mgmin (j) is the lower limit of the output of the jth biomass generator; P mgmax (j) is the upper limit of the output of the jth biomass generator; is the predicted output of the j-th wind farm at time t; The predicted output of the j-th photovoltaic power station at time t; P cmin (j) is the minimum charging power of the j-th energy storage power station; P cmax (j) is the maximum charging power of the jth energy storage station; P dmin (j) is the minimum discharge power of the jth energy storage power station; P dmax (j) is the maximum discharge power of the j-th energy storage station; E(t,j) represents the storage capacity of the j-th energy storage station at time t; E min (j) is the minimum storage capacity of the jth energy storage power station; E max (j) is the maximum storage capacity of the jth energy storage power station.

[0021] Furthermore, the objective function set in step 3 includes renewable energy absorption capacity, economic efficiency and environmental protection objective functions; wherein the renewable energy absorption capacity objective function is specifically: expressed in terms of wind and solar abandonment penalties, as follows: Where: P t W and P t PV They are the wind power and photovoltaic power curtailment at time t, respectively; T represents the total duration; F pv (t), F wp (t), F lc (t) are the penalty for abandoning solar power, the penalty for abandoning wind power and the penalty for load shedding at time t respectively.

[0022] Furthermore, the economic objective function is specifically: the minimization of the annual value of system investment and operating cost is used as the objective function reflecting the economic efficiency of power system planning. The primary investment cost is the sum of the prices of each device under the planned capacity. The annual operating cost of the system includes the annual equipment maintenance and electricity purchase cost: C eco =kC inv +C ope (15) In the formula, C inv , C ope are the annual value of system investment cost and system annual operating cost respectively; r is the discount rate; d is the equipment life cycle; k is the investment cost coefficient;

[0023] The system investment cost includes the investment cost of energy supply device and energy storage device: In the formula, C EG_inv C is the annual value of the investment cost of energy supply equipment; ES_inv The annual investment value of energy storage equipment; is the unit capacity investment cost of energy supply equipment i; P i G is the configuration capacity of energy supply device i; is the unit capacity investment cost of energy storage device j; is the total configuration capacity of energy storage device j; N G N is the number of energy supply equipment. The planned energy supply equipment mainly includes wind power stations, photovoltaic power stations, and biomass generators; S is the number of energy storage devices.

[0024] The system operation cost includes equipment maintenance cost and electricity purchase cost: In the formula, equipment maintenance cost C ope It includes two parts: maintenance cost of energy supply equipment and maintenance cost of energy storage device; is the unit capacity maintenance cost of energy supply equipment i; is the unit capacity maintenance cost of energy storage device j; P t E Purchase electricity for the system; is the price of electricity purchased by the system from the external market; τ is the number of days in a year.

[0025] Furthermore, the environmental protection objective function is specifically: taking the minimum overall carbon emission of the system as the optimization index reflecting the environmental benefits, including two parts: one is the carbon emission in the production process of the input energy itself, that is, the external injected electric energy; the other is the carbon emission generated by the internal energy conversion link: In the formula, c EE _ env is the carbon emission coefficient of the unit; c E_env is the carbon emission coefficient of the external system electricity.

[0026] Furthermore, in step 4, the TOPSIS method, which approximates the ideal solution, is used to solve and sort the model, as follows:

[0027] Step 4.1: Use the vector normalization method to obtain the standardized decision matrix: Let the decision matrix Y of the multi-attribute decision-making problem = {y ij}, the normalized decision matrix Z = {z ij},but:

[0028] Step 4.2: Construct the weighted normalized matrix X = {x ij}:Assume that the decision maker gives w=(w1,w2,...,w n ) T , then: x ij =w j ·z ij ,i=1,...,m;j=1,...,n(21);

[0029] Step 4.3: Determine the ideal solution x * and negative ideal solution x 0 :Assume the ideal solution x * The j-th attribute value of Negative ideal solution x 0 The jth attribute value is Then: Ideal solution Negative ideal solution

[0030] Step 4.4: Calculate the distance of each solution to the ideal solution and the negative ideal solution: Alternative solution x i The distance to the ideal solution is: Alternative x i The distance to the negative ideal solution is:

[0031] Step 4.5: Calculate the queuing index value (i.e. comprehensive evaluation index) of each solution:

[0032] Step 4.6: Arrange the options in descending order based on the comprehensive evaluation index.

[0033] (III) Beneficial effects

[0034] 1. This invention considers the volatility of renewable energy output and proposes a power system optimization configuration method to improve the rural renewable energy absorption capacity based on time series production simulation;

[0035] 2. The present invention can optimize the configuration of key elements of the power system, improve the renewable energy absorption capacity, and ensure the economical and environmentally friendly operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a typical daily load curve diagram of each season of the present invention.

[0037] Figure 2 This is a typical day wind power curve diagram for each season of the present invention.

[0038] Figure 3 It is a typical daily photovoltaic output characteristic curve diagram of each season of the present invention. DETAILED DESCRIPTION

[0039] The present invention is further described below in conjunction with embodiments and / or drawings.

[0040] Example 1

[0041] like Figure 1-3 As shown, a method for optimizing configuration of a power system to promote the consumption of renewable energy in rural areas comprises the following steps:

[0042] Step 1: Construct a model for evaluating the potential of new energy power generation resources in the county;

[0043] In the present invention, the construction of a county-level new energy power generation resource potential assessment model includes conducting potential assessments of renewable power generation resources such as wind power, photovoltaics, and biomass.

[0044] ① Wind power generation potential assessment model: The calculation formula for effective wind energy density is: Where W is the effective wind energy density between the startup wind speed and the shutdown wind speed, and the unit is watts per square meter (W / m 2 ); ρ is the air density, in kilograms per cubic meter (kg / m 3 ); V1 is the startup wind speed, in meters per second (m / s); V2 is the shutdown wind speed, in meters per second (m / s); ρ(V) is the conditional probability distribution density function within the effective wind speed range.

[0045] Wind energy available time. Due to the intermittent and unstable nature of wind energy, the time that wind energy operates within the effective wind speed range is limited in a year. The cumulative number of hours with a wind speed of 3 to 15 m / s throughout the year represents the operating time of the wind turbine, which is called wind energy available time (T), in hours (h). The calculation method can be used as follows: T = ∑N i (2), where T is the available time of wind energy, in hours (h); N is the time period with wind speed of 3–15 m / s within a certain observation period, in hours (h).

[0046] ② Photovoltaic power generation potential assessment model: The stability of solar energy resources plays an important role in resource utilization, and is expressed by the ratio of the maximum to minimum number of days with sunshine hours > 6h in each month: Where K is the solar energy resource stability index, dimensionless; D1, D2, ..., D 12 It is the number of days with sunshine hours > 6h in each month from January to December, unit: d; max and min are standard functions for finding the maximum and minimum values ​​respectively.

[0047] ③ Biomass power generation potential assessment model: Total amount of crop straw: Where CR is the theoretical reserve of crop straw, in kilograms (kg); Qc i is the yield of the i-th crop, in kilograms (kg); p i is the yield per unit area of ​​the i-th type of crop, in kilograms per hectare (kg / hm2); q i is the planting area of ​​the i-th type of crop, in hectares (hm2); r i is the cereal-straw ratio coefficient of the ith category, in percentage (%).

[0048] The theoretical collectible amount of agricultural biomass resources can be calculated using the following formula: TABP is the theoretically collectible amount of the main agricultural biomass resources; P(i) is the economic yield of the ith crop; R(i) is the grain-to-straw ratio of the ith crop.

[0049] The calculation formula for the actual collectible amount of agricultural biomass resources is: PA BP is the actual collectible amount of agricultural biomass resources; F(i) is the actual collectible utilization rate of the i-th crop.

[0050] The calorific value calculation formula of agricultural biomass resources is: NABE is the calorific value of the main agricultural biomass resources, MJ; C(i) is the lower calorific value of the i-th crop, MJ / t.

[0051] The maximum power generation potential of agricultural biomass resources is calculated as follows: ABPP max is the maximum power generation of agricultural biomass resources, MW; K is the overall energy conversion efficiency; T is the annual continuous operation time; the conservative estimate of K is 30%, and the annual operation time is calculated based on the whole year.

[0052] Step 2: Set constraints such as wind power, photovoltaic, biomass power generation output, power system operation, and energy storage configuration;

[0053] In the present invention, specifically: ∑P pv (j,t)+∑P wp (j,t)+∑P mg (j,t)+P lc (t)+∑P d (j,t)=∑P c (j,t)+P l (t)(9), P mgmin (j)≤P mg (j,t)≤P mgmax (j)(10), Formula (9) is the power balance constraint; where P pv (j, t) is the actual output of the j-th PV power station at the t-th moment; P wp (j,t) is the actual output of the j-th wind farm at the t-th moment; P mg (j,t) is the output of the jth biomass generator at the tth moment; P d (j,t) is the discharge power of the jth energy storage power station at time t; P lc (t) is the load shedding power of the system at time t; P c (j,t) is the charging power of the jth energy storage power station at time t; P1(t) is the load power at time t.

[0054] Formula (10) is the upper and lower limit constraints of biomass power generation output; where P mgmin (j) is the lower limit of the output of the jth biomass generator; P mgmax (j) is the upper limit of the output of the jth biomass generator.

[0055] Formula (11) is the output constraint of wind power and photovoltaic power; where P wp (j,t) is the actual output of the j-th wind farm at time t; is the predicted output of the j-th wind farm at the t-th moment; P pv (j,t) is the actual output of the j-th PV power station at time t; The predicted output of the j-th PV plant at time t.

[0056] Formula (12) is the operating state constraint of energy storage, including upper and lower limit constraints of charging power, energy storage discharge power constraint, energy storage charge state constraint, and energy storage capacity constraint; P cmin (j) is the minimum charging power of the j-th energy storage power station; P cmax (j) is the maximum charging power of the jth energy storage station; P dmin (j) is the minimum discharge power of the jth energy storage power station; P dmax (j) is the maximum discharge power of the j-th energy storage station; E(t,j) represents the storage capacity of the j-th energy storage station at time t; E min (j) is the minimum storage capacity of the jth energy storage power station; E max (j) is the maximum storage capacity of the jth energy storage power station.

[0057] Step 3: Set the objective function of power system optimization configuration;

[0058] The present invention mainly includes the following three objectives: ① Renewable energy absorption capacity: expressed by wind and solar abandonment penalties, as follows: Where: P t W and P t PV They are the wind power and photovoltaic power curtailment at time t, respectively; T represents the total duration; Formula (14) is the objective function of the power system optimization configuration model, namely, the load shedding penalty and the renewable energy abandonment penalty; F pv (t), F wp (t), F lc (t) are the penalty for abandoning solar power, the penalty for abandoning wind power and the penalty for load shedding at time t respectively.

[0059] ② Economic efficiency: The objective function that reflects the economic efficiency of power system planning is to minimize the annual value of system investment and operating costs. The primary investment cost is the sum of the prices of each device under the planned capacity. The annual operating cost of the system includes the annual equipment maintenance and electricity purchase costs: C eco =kC inv +C ope (15) In the formula, C inv , C ope are the annual value of system investment cost and system annual operating cost respectively; r is the discount rate; d is the equipment life cycle; k is the investment cost coefficient;

[0060] The system investment cost includes the investment cost of energy supply device and energy storage device: In the formula, C EG_inv C is the annual value of the investment cost of energy supply equipment; ES_inv The annual investment value of energy storage equipment; is the unit capacity investment cost of energy supply equipment i; Pi G is the configuration capacity of energy supply device i; is the unit capacity investment cost of energy storage device j; is the total configuration capacity of energy storage device j; N G N is the number of energy supply equipment. The planned energy supply equipment mainly includes wind power stations, photovoltaic power stations, and biomass generators; S is the number of energy storage devices.

[0061] System operating costs include equipment maintenance costs and electricity purchase costs: In the formula, equipment maintenance cost C ope It includes two parts: maintenance cost of energy supply equipment and maintenance cost of energy storage device; is the unit capacity maintenance cost of energy supply equipment i; is the unit capacity maintenance cost of energy storage device j; P t E Purchase electricity for the system; is the price of electricity purchased by the system from the external market; τ is the number of days in a year.

[0062] ③ Environmental protection: The minimum overall carbon emission of the system is used as the optimization indicator to reflect the environmental benefits, which mainly includes two parts: one is the carbon emission in the production process of the input energy itself, that is, the external injected electric energy; the other is the carbon emission generated by the internal energy conversion link: In the formula, c EE_env is the carbon emission coefficient of the unit; c E_env is the carbon emission coefficient of the external system electricity.

[0063] Step 4: Use the sorting method that approximates the ideal solution to sort the model.

[0064] In the present invention, in step 4, TOPSIS, a ranking method close to the ideal solution, is used to solve and sort the model, as follows:

[0065] (1) Use the vector normalization method to obtain the normalized decision matrix: Let the decision matrix Y of the multi-attribute decision-making problem be {y ij}, the normalized decision matrix Z = {z ij},but:

[0066] (2) Construct the weighted normalized matrix X = {x ij}:Assume that the decision maker gives w=(w1,w2,...,w n ) T , then: x ij =w j ·z ij ,i=1,...,m;j=1,...,n(21);

[0067] (3) Determine the ideal solution x * and negative ideal solution x 0 :Assume the ideal solution x * The j-th attribute value of Negative ideal solution x 0 The jth attribute value is Then: Ideal solution Negative ideal solution

[0068] (4) Calculate the distance between each solution and the ideal solution and the negative ideal solution: Alternative solution x i The distance to the ideal solution is: Alternative x i The distance to the negative ideal solution is:

[0069] (5) Calculate the queuing index value (i.e., comprehensive evaluation index) of each scheme:

[0070] (6) Arrange the options in descending order based on their comprehensive evaluation index.

[0071] The present invention is a method for optimizing the configuration of an electric power system to promote the absorption of renewable energy in rural areas. In use, the present invention constructs a county-level new energy power generation resource potential assessment model; sets constraints such as wind power, photovoltaic, and biomass power generation output, power system operation, and energy storage configuration; sets a power system optimization configuration objective function; and uses a sorting method that approximates an ideal solution to solve and sort the model. The present invention can optimize the configuration of key elements of the power system, improve the absorption capacity of renewable energy, and at the same time improve the stability, economy, and environmental protection of the power system. The present invention has the advantages of considering output volatility, optimizing the configuration of key elements based on time-series production simulation, and improving the absorption capacity.

[0072] Example 2

[0073] like Figure 1-3 As shown, a method for optimizing configuration of a power system to promote the consumption of renewable energy in rural areas comprises the following steps:

[0074] Step 1: Construct a model for evaluating the potential of new energy power generation resources in the county;

[0075] Step 2: Set constraints such as wind power, photovoltaic, biomass power generation output, power system operation, and energy storage configuration;

[0076] Step 3: Set the objective function of power system optimization configuration;

[0077] Step 4: Use the sorting method that approximates the ideal solution to sort the model.

[0078] In the present invention, the technical solution and positive effects of the present invention are further explained below in conjunction with application cases.

[0079] Step 1: Conduct power supply and load characteristics analysis in typical scenarios

[0080] Typical days are divided according to the seasonal characteristics of a county, with March to May as spring, June to August as summer, September to November as autumn, and December and January to February of the following year as winter; the load curves, wind power output characteristic curves, and photovoltaic output characteristic curves of typical days in each season are as follows: Figure 1 , Figure 2 , Figure 3 shown; from Figure 1 It can be seen from the above that the load curve presents a "double peak type", with higher loads in the 8-12 period and the 18-21 period, and the peak characteristics are more prominent in summer; Figure 2 It can be seen that the main characteristics of wind power output are higher output in the 0-8 period and 22-24 period, and lower output in the noon period; Figure 3 It exhibits obvious photovoltaic characteristics, i.e., no output at night and high output at noon. Compared with other seasons, photovoltaic output is higher in autumn.

[0081] Step 2: Set the rural power system model equipment parameters

[0082] The relevant economic and technical parameters of the configured equipment are shown in Table 1. The carbon emission coefficient of the biomass unit is 100gCO2 / kW·h.

[0083] Table 1 Economic and technical parameters of the equipment

[0084]

[0085] Step 3: Analysis of the results of the optimization configuration of the new energy power system

[0086] Based on the typical daily data of each season obtained by division, considering the economy and environmental protection of the system operation, with the goal of improving the renewable energy absorption capacity, the total number of schemes under each scenario is: 648; Table 2 shows the planning results under the optimal scheme 546.

[0087] Table 2 Scheme planning results

[0088]

[0089] The present invention is a method for optimizing the configuration of an electric power system to promote the absorption of renewable energy in rural areas. In use, the present invention constructs a county-level new energy power generation resource potential assessment model; sets constraints such as wind power, photovoltaic, and biomass power generation output, power system operation, and energy storage configuration; sets a power system optimization configuration objective function; and uses a sorting method that approximates an ideal solution to solve and sort the model. The present invention can optimize the configuration of key elements of the power system, improve the absorption capacity of renewable energy, and at the same time improve the stability, economy, and environmental protection of the power system. The present invention has the advantages of considering output volatility, optimizing the configuration of key elements based on time-series production simulation, and improving the absorption capacity.

Claims

1. A method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas, characterized by: The method comprises the following steps: Step 1: Construct a model for evaluating the potential of new energy power generation resources in the county; Step 2: Set wind power, photovoltaic, biomass power generation output, power system operation and energy storage configuration constraints; Step 3: Set the objective function of power system optimization configuration; Step 4: Use the sorting method that approximates the ideal solution to sort the model.

2. A method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas according to claim 1, characterized in that: The step 1 of constructing a county-level new energy power generation resource potential assessment model includes wind power, photovoltaic, and biomass renewable power generation resource potential assessments.

3. A method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas as claimed in claim 2, characterized in that: The wind power generation potential assessment model in step 1 is specifically: the calculation formula for effective wind energy density is: Where W is the effective wind energy density between the startup wind speed and the shutdown wind speed; ρ is the air density; V1 is the startup wind speed; V2 is the shutdown wind speed; ρ(V) is the conditional probability distribution density function within the effective wind speed range.

4. A method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas as claimed in claim 2, characterized in that: The photovoltaic power generation potential assessment model in step 1 is specifically: expressed by the ratio of the maximum value to the minimum value of the number of days with sunshine hours > 6h in each month: Where K is the solar energy resource stability index; D1, D2, ..., D 12 is the number of days with sunshine hours > 6h in each month from January to December; max and min are standard functions for finding the maximum and minimum values ​​respectively.

5. The method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas according to claim 2, characterized in that: The biomass power generation potential assessment model in step 1 is specifically as follows: Total amount of crop straw: Where, CR is the theoretical reserve of crop straw; Qc i is the yield of the i-th crop; p i is the yield per unit area of ​​the i-th type of crop; q i is the planting area of ​​the i-th type of crops; r i is the cereal-to-straw ratio coefficient of the i-th category; the maximum power generation potential of agricultural biomass resources is calculated as follows: ABPP max is the maximum power generation of agricultural biomass resources; K is the overall energy conversion efficiency; T is the annual continuous operation time.

6. The method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas according to claim 1, characterized in that: The constraint condition in step 2 is specifically: ∑P pv (j,t)+∑P wp (j,t)+∑P mg (j,t)+P lc (t)+∑P d (j,t)=∑P c (j,t)+P l (t)(9), P mgmin (j)≤P mg (j,t)≤P mgmax (j)(10), Where P pv (j,t) is the actual output of the j-th PV power station at the t-th moment; P wp (j,t) is the actual output of the j-th wind farm at the t-th moment; P mg (j,t) is the output of the jth biomass generator at the tth moment; P d (j,t) is the discharge power of the jth energy storage power station at time t; P lc (t) is the load shedding power of the system at time t; P c (j,t) is the charging power of the jth energy storage station at time t; P1(t) is the load power at time t; P mgmin (j) is the lower limit of the output of the jth biomass generator; P mgmax (j) is the upper limit of the output of the jth biomass generator; is the predicted output of the j-th wind farm at time t; The predicted output of the j-th photovoltaic power station at time t; P cmin (j) is the minimum charging power of the jth energy storage station; P cmax (j) is the maximum charging power of the jth energy storage station; P dmin (j) is the minimum discharge power of the jth energy storage power station; P dmax (j) is the maximum discharge power of the j-th energy storage station; E(t,j) represents the storage capacity of the j-th energy storage station at time t; E min (j) is the minimum storage capacity of the jth energy storage power station; E max (j) is the maximum storage capacity of the jth energy storage power station.

7. The method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas according to claim 1, characterized in that: The objective function set in step 3 includes renewable energy absorption capacity, economic efficiency and environmental protection objective functions; wherein the renewable energy absorption capacity objective function is specifically: expressed in terms of wind and solar abandonment penalties, as follows: Where: P t W and P t PV They are the wind power and photovoltaic power curtailment at time t, respectively; T represents the total duration; F pv (t), F wp (t), F lc (t) are the penalty for abandoning solar power, the penalty for abandoning wind power and the penalty for load shedding at time t respectively.

8. A method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas as claimed in claim 7, characterized in that: The economic objective function is specifically: the minimization of the annual value of system investment and operating cost is used as the objective function reflecting the economic efficiency of power system planning. The primary investment cost is the sum of the prices of each device under the planned capacity. The annual operating cost of the system includes the annual equipment maintenance and electricity purchase cost: C eco =kC inv +C ope (15) In the formula, C inv , C ope are the annual value of system investment cost and system annual operating cost respectively; r is the discount rate; d is the equipment life cycle; k is the investment cost coefficient; The system investment cost includes the investment cost of energy supply device and energy storage device: In the formula, C EG_inv C is the annual value of the investment cost of energy supply equipment; ES_inv The annual investment value of energy storage equipment; is the unit capacity investment cost of energy supply equipment i; P i G is the configuration capacity of energy supply device i; is the unit capacity investment cost of energy storage device j; is the total configuration capacity of energy storage device j; N G is the number of energy supply equipment; N S is the number of energy storage devices. The system operation cost includes equipment maintenance cost and electricity purchase cost: In the formula, equipment maintenance cost C ope It includes two parts: maintenance cost of energy supply equipment and maintenance cost of energy storage device; is the unit capacity maintenance cost of energy supply equipment i; is the unit capacity maintenance cost of energy storage device j; P t E Purchase electricity for the system; is the price of electricity purchased by the system from the external market; τ is the number of days in a year.

9. A method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas according to claim 7, characterized in that: The environmental protection objective function is specifically: taking the minimum overall carbon emission of the system as the optimization index reflecting the environmental benefits, which includes two parts: one is the carbon emission in the production process of the input energy itself, that is, the external injected electric energy; the other is the carbon emission generated by the internal energy conversion link: In the formula, c EE_env is the carbon emission coefficient of the unit; c E_env is the carbon emission coefficient of the external system electricity.

10. The method for optimizing the configuration of a power system to promote the consumption of renewable energy in rural areas according to claim 1, characterized in that: In step 4, the TOPSIS method, which approximates the ideal solution, is used to solve and sort the model, as follows: Step 4.1: Use the vector normalization method to obtain the standardized decision matrix: Let the decision matrix Y of the multi-attribute decision-making problem = {y ij }, the normalized decision matrix Z = {z ij },but: Step 4.2: Construct the weighted normalized matrix X = {x ij }:Assume that the decision maker gives w=(w1,w2,...,w n ) T , then: x ij =w j ·z ij ,i=1,...,m;j=1,...,n(21); Step 4.3: Determine the ideal solution x * and negative ideal solution x 0 :Assume the ideal solution x * The j-th attribute value of Negative ideal solution x 0 The jth attribute value is Then: Ideal solution Negative ideal solution Step 4.4: Calculate the distance of each solution to the ideal solution and the negative ideal solution: Alternative solution x i The distance to the ideal solution is: Alternative x i The distance to the negative ideal solution is: Step 4.5: Calculate the queuing index value (i.e. comprehensive evaluation index) of each solution: Step 4.6: Arrange the options in descending order based on the comprehensive evaluation index.

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