Biomass energy participates in peak regulation green township power distribution network regional collaborative autonomy method

Abstract

The application provides a kind of biomass energy participates in peak shaving green township power distribution network regional collaborative autonomy method, the method first obtains the source and load prediction data of each regional microgrid of green township, energy supply equipment basic parameters and local grid purchase and sale electricity price data;Then, according to the time scale characteristics of source and load prediction data, an uncertainty model of source and load is established;Then, a double-layer optimization model of green township power distribution network is constructed, including a regional microgrid energy trading price model based on the energy supply and consumption conditions of each regional microgrid as the upper layer strategy, and a two-stage robust optimization model of regional microgrid considering comprehensive constraint conditions with economic efficiency and clean energy consumption rate as optimization objectives based on the uncertainty model of source and load and regional microgrid energy trading price as the lower layer strategy;Finally, the double-layer optimization model of green township power distribution network is iteratively solved, and the distributed energy output, energy storage charging and discharging power and regional interaction power that make the system economic efficiency and clean energy consumption rate optimal are obtained.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation and control technology, and in particular to a green rural power distribution network regional collaborative self-governance method that utilizes biomass energy for peak shaving. Background Technology

[0002] my country's rural areas are rich in renewable energy sources such as solar, wind, hydro, and biomass energy. However, the utilization rate of these new energy sources is low, and problems such as open burning of straw and improper disposal of livestock manure in rural areas not only waste resources but also increase the pressure on the ecological environment, hindering sustainable development in rural areas. New energy power generation technologies are being promoted and applied in rural areas. However, the integration of a large number of distributed energy sources will inevitably affect the stability of township power distribution networks and increase the difficulty of operation and scheduling. Therefore, there is an urgent need to propose an optimized operation method for green township power distribution networks.

[0003] Rural areas have a large migrant population, resulting in significant seasonal load fluctuations. During the Spring Festival, migrant workers return home, causing a surge in load; conversely, after the Spring Festival, the load decreases significantly as the migrant population moves out to work. Because daily loads in rural areas are generally low, the load increase only within short periods, making regional power transformers prone to seasonal overload and even burnout. Furthermore, distributed power sources connected to rural areas, such as small hydropower and rice husk power generation, exhibit strong seasonal characteristics. During the flood season and autumn harvest season, the power generation of small hydropower and rice husk generators increases significantly, easily leading to regional overload due to tidal flow reversal; during the dry season and grain growing season, small hydropower generation decreases, and rice husk power generation ceases operation, resulting in an overall decrease in regional power generation, also easily leading to regional overload. In addition, due to the different types of distributed resources in different rural areas, the synergy of renewable energy between different regions is poor, hindering the absorption of renewable energy and resulting in low utilization rates of new energy sources. Current research focuses primarily on the economic operation of a single region, with relatively few studies considering coordinated operation across multiple regions. On the other hand, there is a lack of research on the optimized operation of new distribution networks containing biomass energy. How to utilize the flexible and controllable characteristics of biomass units to enable biomass energy to participate in grid peak shaving and reduce the risk of regional seasonal heavy overload is an urgent problem to be solved. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving. This method can realize regional collaborative operation of green ecological townships, improve the renewable energy consumption rate in rural areas, and reduce the risk of seasonal heavy overload.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical method: a method for regional collaborative autonomy of green rural power distribution networks with biomass energy participating in peak shaving, comprising:

[0006] Step S1: Obtain source and load forecast data, basic parameters of energy supply equipment, and local power grid purchase and sale price data for microgrids in various areas of green towns. The source and load forecast data includes forecast data for photovoltaic, wind power, small hydropower, regional load power, and daily biogas production and daily rice husk production.

[0007] Step S2: Based on the time scale characteristics of the source load prediction data, establish a source load uncertainty model;

[0008] Step S3: Construct a two-layer optimization model for the green township power distribution network;

[0009] S31. Based on the energy supply and consumption situation of each regional microgrid, a regional microgrid energy trading price model is established as the upper-level strategy of the green township distribution network two-layer optimization model.

[0010] S32, based on the source-load uncertainty model and regional microgrid energy trading price, takes economic efficiency and clean energy consumption rate as optimization objectives, and considers supply and demand balance constraints, biomass energy constraints, unit operation constraints, unit start-up and shutdown constraints and regional heavy overload constraints. It establishes a two-stage robust optimization model for regional microgrid as the lower-level strategy of the two-layer optimization model of green township distribution network.

[0011] Step S4: Iteratively solve the constructed green township power distribution network two-layer optimization model to obtain the distributed energy output, energy storage charging and discharging power and regional interaction power that optimize the system's economy and clean energy absorption rate.

[0012] Furthermore, in step S2, the unit time period for the photovoltaic, wind power, small hydropower, and regional load power prediction data is 1 hour, and a polyhedral uncertainty set is used for modeling; the unit time period for the biogas daily production and rice husk daily production prediction data is 24 hours, and a box uncertainty set is used for modeling. The established source-load uncertainty model is as follows:

[0013]

[0014] In the formula: u is an uncertain variable, including the photovoltaic power vector P PV Wind power vector P WT Small hydropower power vector P SH Load power vector P load Daily biogas production M bio and daily output of rice husks M cha U is the set of uncertain variables; P PV,t P WT,t P SH,t P load,t These represent the actual values ​​of photovoltaic power, wind power, small hydropower power, and load power at time t, respectively. These are the predicted values ​​of photovoltaic power, wind power, small hydropower, and load power at time t, respectively. These are the predicted daily biogas production and daily rice husk production, respectively; λ PV,t , λ WT,t , λ SH,t , λ load,t The uncertainty coefficients for photovoltaic power, wind power, small hydropower power, and load power at time t are respectively, with values ​​of 0 or 1; h PV h WT h SH h load h bio h cha These are the maximum permissible errors for the predicted values ​​of photovoltaic power, wind power, small hydropower, load power, daily biogas production, and daily rice husk production, respectively, and are values ​​between 0 and 1.

[0015] Furthermore, in S31, the established regional microgrid energy trading price model is as follows:

[0016]

[0017] In the formula: D t R t , respectively, represent the purchase-sale ratio and supply-demand ratio of the regional microgrid at time t; N is the number of regional microgrids; These represent the electricity purchased and sold by the nth regional microgrid at time t, including electricity purchased and sold to other regional microgrids and electricity purchased and sold to township distribution networks; b buy,t b sell,t These represent the inter-grid electricity purchase and sale transaction prices at time t; c buy,t c sell,t These represent the purchase and sale prices of electricity from the microgrid to the distribution network at time t.

[0018] Furthermore, in step S3, the optimization objective of the two-stage robust optimization model for the regional microgrid is:

[0019] min F=ω1F1+ω2F2

[0020]

[0021]

[0022] In the formula: F1 is the economic target of the regional microgrid; F2 is the clean energy consumption rate target; ω1 and ω2 are weighting coefficients; a om,x The operation and maintenance cost of unit x; P x,tLet x be the power of unit x; x∈{PV, WT, SH, BG, CG, ESC, ESD}, representing photovoltaic, wind power, small hydropower, biogas, rice husk generator, and energy storage in charging and discharging states, respectively; y∈{BG, CG}; a ss,y Let b be the cost of a single start-up and shutdown of unit y; y , t Let y be the start / stop state of unit y at time t; These represent the electricity sold via micro-online shopping and sales to other regions from the nth region; These represent the electricity purchased and sold from the nth region to the township distribution network.

[0023] Furthermore, in S32, the constraints of the two-stage robust optimization model for the regional microgrid are:

[0024] 1) Supply and demand balance constraints

[0025] P load,t =P PV,t +P WT,t +P SH,t +P BG,t +P CG,t (4)

[0026] In the formula: P BG,t P CG,t These represent the biogas power generation and rice husk power generation at time t, respectively.

[0027] 2) Biomass energy constraints

[0028]

[0029] In the formula: p bio p cha The calorific values ​​of biogas and rice husks are respectively;

[0030] 3) Unit operating constraints

[0031] 0≤P z,t ≤P z,max

[0032] b v,t P v,min ≤P v,t ≤b v,t P v,max

[0033] b ESc,t +b ESd,t ≤1 (6)

[0034] S ES,min ≤S ES,t ≤S ES,max ,S ES,0 =S ES,24

[0035] S ES,t =S ES,t-1 (1-η l o ss )+η ESc P ESc,t -P ESd,t / η ESd

[0036] In the formula: z∈{PV、WT、SH}; P z,t P z,max Let P be the power and maximum value of unit z at time t; v∈{BG, CG, ESC, ESD}; P v,t P v,max P v,min Let v be the power of unit v at time t and its upper and lower limits, respectively; b v,t b represents the on / off state of unit v, a 0-1 variable; ESc,t b ESd,t These represent the charge and discharge states of energy storage; P ESc,t P ESc,max P ESc,min P ESd,t P ESd,max P ESd,min These represent the charging and discharging power of the stored energy at time t and its upper and lower limits, respectively; S ES,t S ES,0 S ES,24 η represents the energy stored at time t, the start time of the cycle, and the end time, respectively; loss η ESc η ESd These are the self-discharge rate of energy storage and the charging and discharging efficiency, respectively.

[0037] 4) Unit start-up and shutdown constraints

[0038] b y,t -b y,t-1 |+|b y,t-1 -b y,t-2 |≤1(7)

[0039] 5) Regional heavy overload constraints

[0040]

[0041]

[0042]

[0043] In the formula: P T,max This represents the maximum power that the regional transformer can withstand.

[0044] Furthermore, in S32, the two-stage robust optimization model for the regional microgrid is as follows:

[0045]

[0046] Where: F is the optimization objective; x and y are the optimization variables for the first and second stages, respectively, and their expressions are:

[0047]

[0048] In the formula: b GB,t b CG,t These represent the start-up and shutdown statuses of biogas power generation and rice husk generator sets, respectively.

[0049] Preferably, in step S4, a two-layer optimization model of the green rural power distribution network is established in the Yalmip toolbox of MATLAB software, and the Cplex solver is called to iteratively solve the model. The solution process includes:

[0050] 1) Input the source and load forecast data, basic parameters of energy supply equipment, and local power purchase and sale prices for each regional microgrid;

[0051] 2) Initialize the maximum number of iterations and the maximum allowable error rate of micro-network sales volume, and define the initial worst-case scenario and micro-network sales volume;

[0052] 3) Substitute the microgrid electricity purchase and sales data and grid electricity price data into the upper-level regional microgrid energy trading price model to obtain the microgrid trading electricity price;

[0053] 4) Substitute the microgrid transaction price into the two-stage robust optimization model of the lower-level regional microgrid, and use the column and constraint generation algorithm to solve the model to obtain the distributed energy output, energy storage charging and discharging power and regional microgrid interaction power, and update the microgrid's electricity purchase and sale power.

[0054] 5) If the electricity purchased and sold by the microgrid is within the allowable error range or the number of iterations reaches the upper limit, then output the currently obtained distributed energy output, energy storage charging and discharging power and regional microgrid interaction power to complete the regional collaborative autonomy of the green township distribution network; otherwise, return to the upper-level regional microgrid energy trading price model for recalculation until the lower-level regional microgrid two-stage robust optimization model converges.

[0055] Preferably, in step S4, the number of iterations is set to 20, and the maximum allowable error rate for micro-network electricity sales is 0.01.

[0056] To address the current lack of management mechanisms for small hydropower stations and biomass power generation units such as biogas and rice husk generators in rural areas, which frequently lead to regional overload, wind power outages, water wastage, and solar power outages in low-voltage distribution areas, this invention proposes a green rural power distribution network regional collaborative autonomy method involving biomass energy in peak shaving. This method aims to improve the level of clean energy consumption and achieve complementary energy operation in rural areas. By scheduling the operation of biomass power units and introducing multiple uncertainties based on the time-scale characteristics of biomass energy, photovoltaics, and wind turbines, this method improves the economy, reliability, and clean energy consumption rate of rural power distribution network operation. Furthermore, the method introduces daily biomass energy production constraints. Under the condition of limited biomass production in rural areas, and based on the grid time-of-use pricing incentives, it encourages biomass energy to be generated more during peak hours and less or not at all during off-peak hours, alleviating the peak shaving pressure on the regional power grid. Moreover, the method incorporates start-up and shutdown costs, effectively solving the problem of frequent start-ups and shutdowns of small hydropower and biomass power generation units. In summary, this invention utilizes the flexible and adjustable characteristics of biomass units to achieve the orderly operation of regional microgrids, reduce the risk of seasonal heavy overloads in the region, and enable energy interaction between regions, effectively improving the absorption capacity of renewable energy in rural areas. Attached Figure Description

[0057] Figure 1 The flowchart illustrates the iterative solution process in the green township power distribution network regional collaborative autonomy method for biomass energy participation in peak shaving provided by this invention.

[0058] Figure 2 This is a diagram illustrating the power supply structure of a green rural power distribution network in an embodiment of the present invention.

[0059] Figure 3 This is a price curve for energy trading in green township microgrids according to an embodiment of the present invention. Detailed Implementation

[0060] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0061] A method for regional collaborative self-governance of green rural power distribution networks with biomass energy participating in peak shaving includes:

[0062] Step S1: Obtain source-load forecast data, basic parameters of energy supply equipment, and local power grid purchase and sale price data for microgrids in various areas of the green township. For example... Figure 2As shown, a green town comprises n regions, each of which includes photovoltaic, wind power, small hydropower, biogas, rice husk distributed energy, and residential load. Therefore, the source-load prediction data in this invention includes prediction data for photovoltaic, wind power, small hydropower, regional load power, and daily biogas and rice husk production. This source-load prediction data can be obtained from the green town smart energy platform, or from new energy power plants and load aggregators, and can be selected according to local conditions.

[0063] Step S2: Based on the time scale characteristics of the source load prediction data, establish a source load uncertainty model.

[0064] The unit time period for photovoltaic, wind power, small hydropower, and regional load power prediction data is 1 hour, and a polyhedral uncertainty set is used for modeling; the unit time period for biogas daily production and rice husk daily production prediction data is 24 hours, and a box uncertainty set is used for modeling. The established source-load uncertainty model is as follows:

[0065]

[0066] In the formula: u is an uncertain variable, including the photovoltaic power vector P PV Wind power vector P WT Small hydropower power vector P SH Load power vector P load Daily biogas production M bio and daily output of rice husks M cha U is the set of uncertain variables; P PV,t P WT,t P SH,t P load,t These represent the actual values ​​of photovoltaic power, wind power, small hydropower power, and load power at time t, respectively. These are the predicted values ​​of photovoltaic power, wind power, small hydropower, and load power at time t, respectively. These are the predicted daily biogas production and daily rice husk production, respectively; λ PV,t , λ WT,t , λ SH,t , λ load,t The uncertainty coefficients for photovoltaic power, wind power, small hydropower power, and load power at time t are respectively, with values ​​of 0 or 1; h PV h WT h SH h load h bio h cha These are the maximum permissible errors for the predicted values ​​of photovoltaic power, wind power, small hydropower, load power, daily biogas production, and daily rice husk production, respectively, and are values ​​between 0 and 1.

[0067] Step S3: Construct a two-layer optimization model for green rural power distribution networks. This model includes an upper-layer strategy and a lower-layer strategy, as detailed below.

[0068] S31, such as Figure 3 As shown, based on the energy supply and consumption situation of each regional microgrid, a regional microgrid energy trading price model is established as the upper-level strategy of the two-layer optimization model of the green township distribution network. The regional microgrid energy trading price model is as follows:

[0069]

[0070] In the formula: D t R t , respectively, represent the purchase-sale ratio and supply-demand ratio of the regional microgrid at time t; N is the number of regional microgrids; These represent the electricity purchased and sold by the nth regional microgrid at time t, including electricity purchased and sold to other regional microgrids and electricity purchased and sold to township distribution networks; b buy,t b sell,t These represent the inter-grid electricity purchase and sale transaction prices at time t; c buy,t c sell,t These represent the purchase and sale prices of electricity from the microgrid to the distribution network at time t.

[0071] S32, based on the source-load uncertainty model and regional microgrid energy trading price, takes economic efficiency and clean energy consumption rate as optimization objectives, and considers supply and demand balance constraints, biomass energy constraints, unit operation constraints, unit start-up and shutdown constraints and regional heavy overload constraints. It establishes a two-stage robust optimization model for regional microgrids as the lower-level strategy of the two-layer optimization model of green township distribution network.

[0072] The optimization objective of the two-stage robust optimization model for regional microgrids is as follows:

[0073] min F=ω1F1+ω2F2

[0074]

[0075]

[0076] In the formula: F1 is the economic target of the regional microgrid; F2 is the clean energy consumption rate target, the smaller the value, the better, in order to reduce wind power, solar power, and biomass energy waste, thereby improving the utilization level of new energy; ω1 and ω2 are weighting coefficients, preferably 0.5; a om,x The operation and maintenance cost of unit x; P x,t Let x be the power of unit x; x∈{PV, WT, SH, BG, CG, ESC, ESD}, representing photovoltaic, wind power, small hydropower, biogas, rice husk generator, and energy storage in charging and discharging states, respectively; y∈{BG, CG}; a ss,yb is the cost of a single start-up and shutdown of unit y; y,t Let y be the start / stop state of unit y at time t; These represent the electricity volume sold via micro-online shopping from the nth region to other regions; These represent the electricity purchased and sold from the nth region to the township distribution network.

[0077] In addition, the constraints of the two-stage robust optimization model for regional microgrids are:

[0078] 1) Supply and demand balance constraints

[0079] P load,t =P PV,t +P WT,t +P SH,t +P BG,t +P CG,t (4)

[0080] In the formula: P BG,t P CG,t These represent the biogas power generation and rice husk power generation at time t, respectively.

[0081] 2) Biomass energy constraints

[0082]

[0083] In the formula: p bio p cha The calorific values ​​of biogas and rice husks are respectively;

[0084] 3) Unit operating constraints

[0085] 0≤P z,t ≤P z,max

[0086] b v,t P v,min ≤P v,t ≤b v,t P v,max

[0087] b ESc,t +b ESd,t ≤1 (6)

[0088] S ES,min ≤S ES,t ≤S ES,max ,S ES,0 =S ES,24

[0089] S ES,t =S ES,t-1 (1-η loss )+η ESc P ESc,t -P ESd,t / ηESd

[0090] In the formula: z∈{PV、WT、SH}; P z,t P z,max Let P be the power and maximum value of unit z at time t; v∈{BG, CG, ESC, ESD}; P v,t P v,max P v,min b represents the power of unit v at time t and its upper and lower limits, respectively; v,t b represents the on / off state of unit v, a 0-1 variable; ESc,t b ESd,t These represent the charge and discharge states of energy storage; P ESc,t P ESc,max P ESc,min P ESd,t P ESd,max P ESd,min These represent the charging and discharging power of the stored energy at time t and its upper and lower limits, respectively; S ES,t S ES,0 S ES,24 η represents the energy stored at time t, the start time of the cycle, and the end time, respectively; loss η ESc η ESd These are the self-discharge rate of energy storage and the charging and discharging efficiency, respectively.

[0091] 4) Unit start-up and shutdown constraints

[0092] |b y,t -b y,t-1 |+|b y,t-1 -b y,t-2 |≤1 (7)

[0093] 5) Regional heavy overload constraints

[0094]

[0095]

[0096]

[0097] In the formula: P T,max This represents the maximum power that the regional transformer can withstand.

[0098] Furthermore, in S32, the two-stage robust optimization model for the regional microgrid is as follows:

[0099]

[0100] Where: F is the optimization objective; x and y are the optimization variables for the first and second stages, respectively, and their expressions are:

[0101]

[0102] In the formula: b GB,t b CG,t These represent the start-up and shutdown statuses of biogas power generation and rice husk generator sets, respectively.

[0103] Step S4 involves iteratively solving the constructed two-layer optimization model of the green township power distribution network to obtain the distributed energy output, energy storage charging and discharging power, and regional interaction power that optimize the system's economy and clean energy absorption rate. Specifically, a two-layer optimization model of the green township power distribution network is established in the Yalmip toolbox of MATLAB software, and the Cplex solver is used to iteratively solve the model, such as... Figure 1 As shown, the solution process includes:

[0104] 1) Input the source and load forecast data, basic parameters of energy supply equipment and the purchase and sale price of electricity in each regional microgrid.

[0105] 2) Initialize the maximum number of iterations to 20 and the maximum allowable error rate of micro-network purchase and sales volume to 0.01. Define the initial worst-case scenario and initialize the micro-network purchase and sales volume based on the source load prediction data.

[0106] 3) Substitute the microgrid electricity purchase and sales data and grid electricity price data into the upper-level regional microgrid energy trading price model to obtain the microgrid trading electricity price.

[0107] 4) Substitute the microgrid transaction price into the two-stage robust optimization model of the lower-level regional microgrid, and use the column and constraint generation algorithm to solve the model to obtain the distributed energy output, energy storage charging and discharging power and regional microgrid interaction power, and update the microgrid purchase and sale electricity.

[0108] 5) If the electricity purchased and sold by the microgrid is within the allowable error range or the number of iterations reaches the upper limit, then output the currently obtained distributed energy output, energy storage charging and discharging power and regional microgrid interaction power to complete the regional collaborative autonomy of the green township distribution network; otherwise, return to the upper-level regional microgrid energy trading price model for recalculation until the lower-level regional microgrid two-stage robust optimization model converges.

[0109] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

[0110] To facilitate understanding by those skilled in the art of the improvements of this invention over the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this invention.

Claims

1. A method for regional collaborative self-governance of green rural power distribution networks with biomass energy participating in peak shaving, characterized in that, include: Step S1: Obtain source and load forecast data, basic parameters of energy supply equipment, and local power grid purchase and sale price data for microgrids in various areas of green towns. The source and load forecast data includes forecast data for photovoltaic, wind power, small hydropower, regional load power, and daily biogas production and daily rice husk production. Step S2: Based on the time scale characteristics of the source load prediction data, establish a source load uncertainty model; The unit time period for photovoltaic, wind power, small hydropower, and regional load power prediction data is 1 hour, and a polyhedral uncertainty set is used for modeling; the unit time period for biogas daily production and rice husk daily production prediction data is 24 hours, and a box uncertainty set is used for modeling. The established source-load uncertainty model is as follows: (1) In the formula: u is an uncertain variable, including the photovoltaic power vector. Wind power vector Small hydropower power vector Load power vector Daily biogas production and daily output of rice husks ; U is a set of uncertain variables; , , , These represent the actual values ​​of photovoltaic power, wind power, small hydropower power, and load power at time t, respectively. , , , These are the predicted values ​​of photovoltaic power, wind power, small hydropower, and load power at time t, respectively. , These are the predicted daily biogas production and daily rice husk production, respectively. , , , These are the uncertainty coefficients for photovoltaic power, wind power, small hydropower, and load power at time t, respectively, with values ​​of 0 or 1; , , , , , These are the maximum permissible errors for the predicted values ​​of photovoltaic power, wind power, small hydropower, load power, daily biogas production, and daily rice husk production, respectively, and are values ​​between 0 and 1. Step S3: Construct a two-layer optimization model for the green township power distribution network; S31. Based on the energy supply and consumption situation of each regional microgrid, a regional microgrid energy trading price model is established as the upper-level strategy of the green township distribution network two-layer optimization model. S32, based on the source-load uncertainty model and regional microgrid energy trading price, takes economic efficiency and clean energy consumption rate as optimization objectives, and considers supply and demand balance constraints, biomass energy constraints, unit operation constraints, unit start-up and shutdown constraints and regional heavy overload constraints. It establishes a two-stage robust optimization model for regional microgrid as the lower-level strategy of the two-layer optimization model of green township distribution network. Step S4: Iteratively solve the constructed green township power distribution network two-layer optimization model to obtain the distributed energy output, energy storage charging and discharging power and regional interaction power that optimize the system's economy and clean energy absorption rate.

2. The method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving according to claim 1, characterized in that: In S31, the established regional microgrid energy trading price model is as follows: (2) In the formula: D t R t , respectively, represent the purchase-sale ratio and supply-demand ratio of the regional microgrid at time t; N is the number of regional microgrids; , These represent the electricity purchased and sold by the nth regional microgrid at time t, including electricity purchased and sold to other regional microgrids and electricity purchased and sold to township distribution networks; b buy,t b sell,t These represent the inter-grid electricity purchase and sale transaction prices at time t; c buy,t c sell,t These represent the purchase and sale prices of electricity from the microgrid to the distribution network at time t.

3. The method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving according to claim 2, characterized in that: In step S3, the optimization objective of the two-stage robust optimization model for the regional microgrid is: (3) In the formula: To optimize the objectives; F1 is the economic target for regional microgrids; F2 is the clean energy consumption rate target; , a is the weighting coefficient; om,x The operation and maintenance cost of unit x; P x,t Let x be the power of unit x; x∈{PV, WT, SH, BG, CG, ESC, ESD}, representing photovoltaic, wind power, small hydropower, biogas, rice husk generator, and energy storage in charging and discharging states, respectively; y∈{BG, CG}; a ss,y b is the cost of a single start-up and shutdown of unit y; y,t Let y be the start / stop state of unit y at time t; The start-up and shutdown status of unit y at time t-1; , These represent the electricity volume sold via micro-online shopping from the nth region to other regions; , These represent the electricity purchased and sold from the nth region to the township distribution network.

4. The method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving according to claim 3, characterized in that: In S32, the constraints of the two-stage robust optimization model for the regional microgrid are: 1) Supply and demand balance constraints (4) In the formula: P BG,t P CG,t These represent the biogas power generation and rice husk power generation at time t, respectively. 2) Biomass energy constraints (5) In the formula: p bio p cha The calorific values ​​of biogas and rice husks are respectively; 3) Unit operating constraints (6) In the formula: z∈{PV、WT、SH}; P z,t P z,max Let P be the power and maximum value of unit z at time t; v∈{BG, CG, ESC, ESD}; P v,t P v,max P v,min b represents the power of unit v at time t and its upper and lower limits, respectively; v,t b represents the on / off state of unit v, a 0-1 variable; ESc,t b ESd,t These represent the charge and discharge states of energy storage; P ESc,t P ESc,max P ESc,min P ESd,t P ESd,max P ESd,min These represent the charging and discharging power of the stored energy at time t and its upper and lower limits, respectively; S ES,t S ES,0 S ES,24 η represents the energy stored at time t, the start time of the cycle, and the end time, respectively; loss η ESc η ESd These are the self-discharge rate of energy storage and the charging and discharging efficiency, respectively. 4) Unit start-up and shutdown constraints (7) In the formula: b y,t Let y be the start / stop state of unit y at time t; The start-up and shutdown status of unit y at time t-1; The start-up and shutdown status of unit y at time t-2; 5) Regional heavy overload constraints (8) In the formula: P T,max This represents the maximum power that the regional transformer can withstand.

5. The method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving according to claim 4, characterized in that: In S32, the two-stage robust optimization model for the regional microgrid is as follows: (9) Where: F is the optimization objective; x0 and y0 are the optimization variables for the first and second stages, respectively, and their expressions are: (10) In the formula: b BG,t b CG,t These represent the start-up and shutdown statuses of biogas power generation and rice husk generator sets, respectively.

6. The method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving according to claim 5, characterized in that: In step S4, a two-layer optimization model of the green rural power distribution network is established in the Yalmip toolbox of MATLAB software. The Cplex solver is then used to iteratively solve the model. The solution process includes: 1) Input the source and load forecast data, basic parameters of energy supply equipment, and local power grid purchase and sale prices for each regional microgrid; 2) Initialize the maximum number of iterations and the maximum allowable error rate of micro-network sales volume, and define the initial worst-case scenario and micro-network sales volume; 3) Substitute the microgrid electricity purchase and sales data and grid electricity price data into the upper-level regional microgrid energy trading price model to obtain the microgrid trading price; 4) Substitute the microgrid transaction price into the two-stage robust optimization model of the lower-level regional microgrid, and use the column and constraint generation algorithm to solve the model to obtain the distributed energy output, energy storage charging and discharging power and regional microgrid interaction power, and update the microgrid's electricity purchase and sale power. 5) If the electricity purchased and sold by the microgrid is within the allowable error range or the number of iterations reaches the upper limit, then output the currently obtained distributed energy output, energy storage charging and discharging power and regional microgrid interaction power to complete the regional collaborative autonomy of the green township distribution network; otherwise, return to the upper-level regional microgrid energy trading price model for recalculation until the lower-level regional microgrid two-stage robust optimization model converges.

7. The method for regional collaborative self-governance of green township power distribution networks with biomass energy participating in peak shaving according to claim 6, characterized in that: In step S4, the number of iterations is set to 20, and the maximum allowable error rate for the electricity sales volume of the micro-network is 0.01.

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