A two-layer optimization scheduling method based on a master-slave game theory approach for multi-community microgrid systems

Abstract

This invention discloses a two-layer optimization scheduling method based on a master-slave game theory approach for multi-community microgrid systems. The method includes: establishing a network architecture and model for the community microgrid system; constructing a two-layer master-slave game theory model for the multi-community microgrid system; constructing a low-carbon management framework and model for the multi-community microgrid system; constructing an upper-level regional dispatch center optimization decision model; constructing a lower-level community microgrid system operation decision model; constructing a master-slave game theory optimization model for the multi-community microgrid system based on the upper-level regional dispatch center optimization decision model and the lower-level community microgrid system operation decision model; and solving the master-slave game theory optimization model using an optimization algorithm. This invention proposes a two-layer optimization scheduling method based on a master-slave game theory approach to reduce the overall operating cost of the system, achieve supply and demand balance among the community microgrids, and coordinate the economic and low-carbon benefits of the multi-community microgrid system operation.

Description

TECHNICAL FIELD

[0001] The application relates to a one-master multi-slave game double-layer optimization scheduling method of a multi-community microgrid system, in particular to a one-master multi-slave game double-layer optimization scheduling method of a multi-community microgrid system considering energy interaction and carbon trading, and belongs to the technical field of low-carbon economic scheduling of a multi-community microgrid system. BACKGROUND

[0002] With the progress of science and technology, social development and population growth, the global energy demand is increasing sharply, leading to the exhaustion of traditional fossil energy mainly composed of coal and oil, and the environmental pollution problem caused by energy consumption is also becoming increasingly serious. In addition, with the deepening of the urbanization process in China, the sharp increase in energy demand in urban areas has caused great pressure on the stable operation of energy supply infrastructure, and the increasingly complex energy demand has brought great challenges to the energy dispatching department centered on electricity. Under the above background, the construction of a multi-community microgrid system centered on electricity, covering distributed clean energy such as wind power and photovoltaic power, supporting the mutual coordination and complementation of different forms of energy, has become an effective way to solve the energy crisis and environmental problems. However, in the current optimization scheduling of the multi-community microgrid system, the conflict between the overall energy purchasing cost of the region and the individual interest of the microgrid is not fully considered, and the problems of carbon emission accounting, carbon emission right trading and the like of the terminal comprehensive energy system under the background of the carbon emission right trading market are not considered. SUMMARY

[0003] The application provides a one-master multi-slave game double-layer optimization scheduling method of a multi-community microgrid system, which is based on virtual carbon flow and actual carbon flow, analyzes the carbon emission sources of the community microgrid system in detail from the perspective of energy flow, establishes a refined model considering direct carbon emission accounting and indirect carbon emission accounting of the user side, and builds a one-master multi-slave game optimization model of the multi-community microgrid system considering energy interaction and carbon trading on the basis of comprehensively considering economic benefits and low-carbon benefits.

[0004] The technical scheme of the application is as follows: a one-master multi-slave game double-layer optimization scheduling method of a multi-community microgrid system, which comprises the following steps: establishing a network architecture and a model of the community microgrid system; building a one-master multi-slave game double-layer model of the multi-community microgrid system; building a low-carbon management framework and a model of the multi-community microgrid system; building an upper-layer regional dispatching center optimization decision model with the minimum total operation cost of the region in a single dispatching day as the target; building a lower-layer community microgrid system operation decision model with the minimum operation cost of the community microgrid in a single dispatching day as the target; building a one-master multi-slave game optimization model of the multi-community microgrid system according to the upper-layer regional dispatching center optimization decision model and the lower-layer community microgrid system operation decision model; and solving the one-master multi-slave game optimization model by using an optimization algorithm.

[0005] The community micro-grid system network architecture and model are established, including: establishing a data information management unit model, an energy supply unit model, a terminal multi-element load unit model, a coupling device unit model, and an energy storage device unit model.

[0006] The multi-community micro-grid system one master multi-slave game double-layer model is constructed as:

[0007]

[0008] In the game double-layer model G, Z is a game subject set, including a regional dispatch center RDC and each community micro-grid CIES(i); S represents a strategy set of the game subject, the operation strategy set S of the upper regional dispatch center RDC including the energy purchasing amount x buy of each micro-grid, i,j the carbon trading amount CER i,j information, the operation strategy set S of each community micro-grid in the lower layer CIES(i) including the device output x equipment in the community micro-grid i and the load amount x load after demand response RDC (i) parameter; F is a payment function of the game subject, including a payment function f CIES(i) of the regional dispatch center and a payment function f RDC of each community micro-grid.

[0009] The low-carbon management framework and model of the multi-community micro-grid system are constructed, including: a regional user initial carbon emission allocation model determined by a carbon trading regulatory agency based on historical energy consumption of the regional quarterly; a community micro-grid system carbon emission accounting model established based on carbon flow for carbon emission source analysis; and a carbon emission right trading model taking the minimum carbon trading procedure cost as the target and ensuring that the carbon emission rights are preferentially and fully traded among the regional community micro-grids; wherein the community micro-grid system carbon emission accounting model includes a direct carbon emission accounting model and an indirect carbon emission accounting model.

[0010] The upper regional dispatch center optimization decision model is constructed by taking the minimum total operation cost of the single dispatch day regional as the target, including: a multi-community micro-grid system daily operation cost target function composed of energy purchasing cost, energy interaction loss cost, and carbon trading procedure cost; constructing an optimization decision model constraint condition; and the optimization decision model constraint condition includes: energy purchasing constraint, community micro-grid inter-energy interaction constraint, and community micro-grid energy supply and demand margin constraint.

[0011] The multi-community micro-grid system daily operation cost target function f RDC is composed of energy purchasing cost, energy interaction loss cost, and carbon emission right trading procedure cost, and is as follows:

[0012]

[0013] In the formula: respectively total purchasing cost, community microgrid energy interaction loss cost, carbon emission trading procedure cost; T, N m respectively total scheduling period number, community microgrid number in the region; respectively represent the price of electricity, gas and heat energy in the t period; respectively represent the electricity, gas and heat purchasing quantity of the microgrid i in the t period; respectively represent the electricity and gas energy interaction quantity between the microgrid i and the microgrid j; respectively represent the electricity and gas energy interaction loss coefficient between the community microgrids; a and b respectively represent the service cost coefficient of the carbon trading between the community microgrid and the national carbon trading market and the community microgrid; is the potential interaction quantity between the community microgrid i and the national carbon emission trading market; represents the carbon emission trading quantity between the community microgrids i and j.

[0014] The single scheduling day community microgrid operation cost minimum is taken as the target to build a lower layer community microgrid system operation decision model, which comprises: a community microgrid daily operation cost target function composed of purchasing cost, equipment operation and maintenance cost, comprehensive demand response compensation cost, energy interaction income and carbon trading income; a constraint condition of the operation decision model is built; the constraint condition of the operation decision model comprises: equipment output constraint, community microgrid energy power balance constraint and community microgrid energy supply and demand margin constraint.

[0015] The community microgrid daily operation cost target function is composed of purchasing cost , equipment operation and maintenance cost , comprehensive demand response compensation cost , energy interaction income and carbon trading income

[0016]

[0017] In the formula: respectively represent the price of electricity, gas and heat energy in the t period; respectively represent the electricity, gas and heat purchasing quantity of the microgrid i in the t period; respectively represent the scene serial number, corresponding scene probability and total scene set; respectively represent the operation and maintenance cost coefficient of each equipment in the community microgrid; respectively represent the photovoltaic and wind power of the microgrid i in the t period scene s; respectively represent the natural gas power consumed by the microturbine and gas refrigeration machine of the microgrid i in the t period; respectively represent the input power of the electric refrigeration machine and heat absorption type refrigeration machine in the microgrid i in the t period;​​ These represent the charging and discharging power of electrical, gas, heat, and cold energy storage within microgrid i during time period t and scenario s, respectively. These represent the longitudinal demand response compensation coefficients for electricity, gas, and heat loads of microgrid i, respectively. These represent the horizontal demand response compensation coefficients for electricity and heat in microgrid i, respectively. These represent the upward shift of the electrical, gas, and heat loads of microgrid i during time period t; These represent the downward shift of the electricity, gas, and heat loads of microgrid i during time period t; The amount of electrical load reduction caused by the replacement of electrical energy with gas energy in microgrid i during time period t; The amount of gas load reduced by microgrid i when gas energy is replaced by electrical energy during time period t; The amount of electrical load reduction caused by the replacement of electrical energy with heat energy in microgrid i during time period t; These represent the reduction in heat load caused by the replacement of thermal energy with electrical energy in microgrid i during time period t; These are the electricity and gas energy exchange prices between community microgrids during time period t; These represent the electrical and gas energy interaction quantities between microgrid i and microgrid j, respectively. Indicates the market price of carbon emission rights; To act as an agent for the regional dispatch center to handle the potential interaction between the community microgrid and the national carbon emission trading market; This represents the carbon emission trading volume between community microgrids i and j.

[0018] The master-slave game optimization model is as follows:

[0019]

[0020] In the formula: These represent the energy consumed in community micro-grid shopping, the energy exchange volume between microgrids, and the carbon trading volume, respectively. These represent the load and equipment output after the response to the optimal operation decision of the lower-level community microgrid; These represent the power output of devices within the community microgrid i and the load after demand response, respectively. These represent the energy purchased by the community microgrid, the energy exchange between community microgrids, and the carbon trading volume between community microgrids when the upper-level regional dispatch center issues the optimal dispatch instruction, respectively.

[0021] A solution method using an upper-level particle swarm optimization algorithm nested with a lower-level Gurobi solver is employed to solve the constructed master-slave game optimization model.

[0022] The beneficial effects of this invention are:

[0023] (1) Based on the consideration of the longitudinal and lateral demand response of the comprehensive load of electricity, gas, heat and cooling, this invention establishes a user-side demand management framework for community microgrid system, which improves the flexibility of energy supply and demand of the system.

[0024] (2) Based on virtual carbon flow and actual carbon flow, this invention analyzes the actual carbon emissions in the community microgrid system from the perspective of energy flow, and establishes a refined model that considers direct carbon emission accounting and indirect carbon emission accounting. It can accurately calculate the carbon emissions generated during the operation of the community microgrid system, so as to support each community microgrid to participate in the carbon emission trading market as a carbon trading entity.

[0025] (3) Based on the energy supply and demand characteristics and actual carbon emission situation of each community microgrid, this invention establishes an energy interaction and carbon emission trading model between community microgrids, analyzes the competitive game relationship between the regional dispatch center and the microgrid operators at the community level, and proposes a two-layer optimization dispatch method based on a master-slave game to reduce the overall operating cost of the system, achieve the supply and demand balance of each community microgrid, and coordinate the economic and low-carbon benefits of the operation of the multi-community microgrid system. Attached Figure Description

[0026] Figure 1 This is a diagram of the two-layer optimized scheduling framework for the multi-community microgrid system of this invention;

[0027] Figure 2 This is a schematic diagram of the network architecture of the community integrated energy system constructed in this invention;

[0028] Figure 3 This is a schematic diagram of the two-layer game interaction framework of the community microgrid system constructed in this invention;

[0029] Figure 4 A schematic diagram of the low-carbon management framework of the community microgrid system constructed in this invention;

[0030] Figure 5 This is a schematic diagram of carbon flow and carbon source analysis according to the present invention;

[0031] Figure 6 This is a schematic diagram of carbon emission trading according to the present invention;

[0032] Figure 7 The flowchart shows the solution process for the master-slave game two-layer optimization model proposed in this invention.

[0033] Figure 8 This is a graph showing the output curves of wind power and photovoltaic power in a specific example of the present invention;

[0034] Figure 9 This is a real-time energy price curve chart under a specific example of the present invention;

[0035] Figure 10The above are simulation results of demand response in a residential community under a specific example of the present invention.

[0036] Figure 11 This is a specific example of the energy supply and demand balance diagram in a residential community according to the present invention;

[0037] Figure 12 This is a schematic diagram of energy interaction and carbon trading between community microgrids in a specific embodiment of the present invention. Detailed Implementation

[0038] The invention will be further described below with reference to the accompanying drawings and embodiments, but the scope of the invention is not limited to the description.

[0039] like Figures 1-12 As shown, a two-layer optimization scheduling method for a multi-community microgrid system using a master-slave game theory approach includes: establishing the network architecture and model of the community microgrid system; constructing a two-layer model of the multi-community microgrid system using a master-slave game theory approach; constructing a low-carbon management framework and model for the multi-community microgrid system; constructing an upper-level regional dispatch center optimization decision model with the objective of minimizing the total regional operating cost on a single scheduling day; constructing a lower-level community microgrid system operation decision model with the objective of minimizing the community microgrid operating cost on a single scheduling day; constructing a master-slave game theory optimization model for the multi-community microgrid system based on the upper-level regional dispatch center optimization decision model and the lower-level community microgrid system operation decision model; and solving the master-slave game theory optimization model using an optimization algorithm.

[0040] Furthermore, the establishment of the community microgrid system network architecture and model includes: establishing a data information management unit model, an energy supply unit model, a terminal multi-load unit model, a coupling equipment unit model, and an energy storage equipment unit model.

[0041] Furthermore, the construction of the multi-community microgrid system's one-master-many-slave game-theoretic two-layer model is as follows:

[0042]

[0043] In the two-layer game theory model G: Z is the set of game players, including the regional dispatch center RDC and each community microgrid CIES(i); S represents the set of strategies of the game players, and the set of operating strategies of the upper-level regional dispatch center Si. RDC Including various micro-online shopping energy x buy (i) Energy interaction quantity x between microgrids i and j i,j Carbon trading volume (CER) i,j Information, the operational strategy set of each lower-level community micronetwork S CIES(i) Including the output of devices within the community microgrid. equipment (i) and load after demand response x load (i) Parameters; F is the payoff function of the game players, including the regional dispatch center payoff function f.RDC and the payment function f of each community micro-network CIES(i) .

[0044] Furthermore, the construction of the low-carbon management framework and model for the multi-community microgrid system includes: a regional user initial carbon emission quota model determined by the carbon trading regulatory agency based on the region's quarterly historical energy consumption; a community microgrid system carbon emission accounting model established based on carbon flow carbon emission source analysis; and a carbon emission rights trading model that aims to minimize carbon trading transaction costs and ensure priority and full trading among community microgrids within the region. The community microgrid system carbon emission accounting model includes a direct carbon emission accounting model and an indirect carbon emission accounting model.

[0045] Furthermore, the optimization decision-making model for the upper-level regional dispatch center is constructed with the goal of minimizing the total operating cost of a single dispatch day. This includes: constructing an objective function for the daily operating cost of the multi-community microgrid system, which consists of energy purchase cost, energy interaction loss cost, and carbon trading transaction cost; constructing constraints for the optimization decision-making model; and the constraints for the optimization decision-making model include: energy purchase constraints, energy interaction constraints between community microgrids, and energy supply and demand margin constraints of community microgrids.

[0046] Furthermore, the objective function for the daily operating cost of the multi-community microgrid system, which comprises energy purchase costs, energy interaction loss costs, and carbon emission trading transaction costs, is further defined. for:

[0047]

[0048] In the formula: These are the total energy purchase cost, the cost of energy exchange losses between community microgrids, and the transaction cost of carbon emission trading; T, N m These represent the total number of scheduling periods and the number of community microgrids within the region, respectively; p p (t), p g (t), p h (t) represents the price of electricity, gas, and heat energy during time period t, respectively; P buy (t,i), G buy (t,i),H buy (t,i) represent the electricity, gas, and heat energy of microgrid i in time period t, respectively; P i,j (t), G i,j (t) represent the electrical and gas energy interaction quantities between microgrid i and microgrid j, respectively; , a and b are the energy loss coefficients for electricity and gas interaction between community microgrids, respectively; a and b are the service cost coefficients for carbon trading between community microgrids and the national carbon trading market, and between community microgrids, respectively. The regional dispatch center acts as an agent for the potential interaction between the community microgrid and the national carbon emission trading market; CER i,jThis represents the carbon emission trading volume between community microgrids i and j; the carbon emission trading transaction cost is the carbon trading transaction cost.

[0049] Furthermore, with the goal of minimizing the daily operating cost of a single community microgrid, a decision-making model for the lower-level community microgrid system is constructed. This model includes: a daily operating cost objective function for the community microgrid, consisting of energy purchase cost, equipment operation and maintenance cost, comprehensive demand response compensation cost, energy interaction revenue, and carbon trading revenue; and constraints for the operation decision-making model, including: equipment output constraints, community microgrid energy power balance constraints, and community microgrid energy supply and demand margin constraints.

[0050] Furthermore, from the cost of energy purchase Equipment operation and maintenance costs Comprehensive demand response compensation cost Energy interaction benefits Carbon trading revenue Objective function of daily operating cost of the community microgrid for:

[0051]

[0052] In the formula: p p (t), p g (t), p h (t) represents the price of electricity, gas, and heat energy during time period t, respectively; P buy (t,i), G buy (t,i),H buy (t,i) represent the electricity, gas, and heat energy of microgrid i in time period t, respectively; s, γ s N s These are the scene number, the probability of the corresponding scene, and the total set of scenes, respectively. These are the operation and maintenance cost coefficients for each device in the community microgrid; P pv (t,i,s),P wt (t,i,s) represent the power of photovoltaic and wind power of microgrid i in scenario s during time period t, respectively; G mt (t,i), G gc (t,i) represent the natural gas power consumed by the micro-turbine and gas-fired chiller of microgrid i during time period t, respectively; P ec (t,i),H ac (t,i) represent the input power of the electric chiller and the heat absorption chiller in microgrid i during time period t, respectively; P c (t,i,s),P d (t,i,s),G c (t,i,s),G d (t,i,s),H c(t,i,s),H d (t,i,s),C c (t,i,s),C d (t,i,s) represent the charging and discharging power of electricity, gas, heat, and cold energy storage within microgrid i in scenario s during time period t; α p (i), α g (i), α h (i) represent the longitudinal demand response compensation coefficients for electricity, gas, and heat loads of microgrid i, respectively; β e,g (i), β e,h (i) represent the horizontal demand response compensation coefficients for electricity-gas and electricity-heat of microgrid i, respectively; These represent the upward shift of the electrical, gas, and heat loads of microgrid i during time period t; These represent the downward shift of the electricity, gas, and heat loads of microgrid i during time period t; The amount of electrical load reduction caused by the replacement of electrical energy with gas energy in microgrid i during time period t; The amount of gas load reduced by microgrid i when gas energy is replaced by electrical energy during time period t; The amount of electrical load reduction caused by the replacement of electrical energy with heat energy in microgrid i during time period t; These represent the reduction in heat load caused by the replacement of thermal energy with electrical energy in microgrid i during time period t; These represent the electricity and gas energy exchange prices between community microgrids during time period t; P i,j (t), G i,j (t) represent the electrical and gas energy interaction quantities between microgrid i and microgrid j, respectively; p CER Indicates the market price of carbon emission rights; The regional dispatch center acts as an agent for the potential interaction between the community microgrid and the national carbon emission trading market; CER i,j This represents the carbon emission trading volume between community microgrids i and j.

[0053] Furthermore, the master-slave game optimization model is as follows:

[0054]

[0055] In the formula: x buy (i), x i,j CER i,j These represent the energy consumed in community micro-grid shopping, the energy exchange volume between microgrids, and the carbon trading volume, respectively. These represent the load and equipment output after the response to the optimal operation decision of the lower-level community microgrid; x load (i), x equipment (i) represent the power output of equipment and the load after demand response within community microgrid i, respectively. These represent the energy purchased by the community microgrid, the energy exchange volume between community microgrids, and the carbon trading volume between community microgrids when the upper-level regional dispatch center issues the optimal dispatch instruction, respectively.

[0056] Furthermore, a solution method is adopted to solve the constructed one-master-many-slave game optimization model by nesting the upper-level particle swarm optimization algorithm with the lower-level Gurobi solver.

[0057] The following is a detailed description of an optional embodiment of the present invention.

[0058] Step S1: Establish the network architecture and model of the community microgrid system, including data information management unit, energy supply unit, terminal multi-load unit, coupling equipment unit, and energy storage equipment unit;

[0059] Step S2: Construct a two-layer game model for a multi-community microgrid system, which includes an upper-layer regional dispatch center that acts as the game leader and is responsible for centralized optimization, and a lower-layer community microgrid that acts as the game follower and focuses on distributed autonomy.

[0060] Step S3: Construct a low-carbon management framework and model for a multi-community microgrid system, including an initial carbon emission quota model for regional users determined by the carbon trading regulatory agency based on the region's quarterly historical energy consumption; an indirect carbon emission accounting model established based on virtual carbon flows for indirect carbon emission source analysis; a direct carbon emission accounting model established based on actual carbon flows for direct carbon emission source analysis; and a carbon emission rights trading model that aims to minimize carbon trading transaction costs and ensure priority and full trading among community microgrids within the region.

[0061] Step S4: Based on the idea of ​​centralized optimization, with the goal of minimizing the total regional operating cost on a single scheduling day, construct an optimization decision model for the upper-level regional dispatch center to the lower-level community microgrid, including energy purchase power, energy interaction power, and carbon trading volume;

[0062] Step S5: Based on the concept of distributed autonomy, and with the goal of minimizing the daily operating cost of a single community microgrid, construct a lower-level community microgrid system operation decision model that includes energy purchase cost, operation and maintenance cost, comprehensive demand response compensation cost, energy interaction benefits, and carbon trading benefits.

[0063] Step S6: Combining the optimization decision-making models for energy purchase power, energy interaction power, and carbon trading volume of the upper-level regional dispatch center established in Steps S4 and S5 with the operation decision-making model of the lower-level community microgrid system, construct a multi-community microgrid system master-slave game optimization model that considers energy interaction and carbon trading.

[0064] Step S7: Solve the one-master-many-slave game optimization model constructed in Step S6 using the method of nesting the upper-level particle swarm optimization algorithm with the lower-level Gurobi solver.

[0065] The overall framework diagram of the present invention is as follows: Figure 1 As shown. Further, the specific steps of the method in the above optional embodiment are as follows:

[0066] Step S1 specifically involves: (e.g.) Figure 2 As shown, a community microgrid system network architecture and model are constructed, comprising a data information management unit, an energy supply unit, terminal multi-load units, coupling equipment units, and energy storage equipment units, wherein:

[0067] 1.1) The data information management unit model is as follows:

[0068] The data information management unit collects internal information of the community microgrid system and uploads it to the regional dispatch center. At the same time, it receives energy purchase, energy interaction and carbon trading instructions from the regional dispatch center to optimize the power distribution among the various equipment units in the system and realize the safe, economical and low-carbon operation of the microgrid. Among them, equipment units refer to coupling equipment units and energy storage equipment units.

[0069] 1.2) The energy supply unit model is as follows:

[0070] The actual output of wind and solar power is the sum of the day-ahead forecast data and the forecast error, and the forecast error follows a normal distribution. The corresponding model is as follows:

[0071]

[0072]

[0073] In the formula: e wt (t,i),e pv (t,i) represent the prediction errors of wind power and photovoltaic power output of microgrid i in time period t, respectively; P wt (t,i) These represent the actual wind power output and the day-ahead forecast value of microgrid i during time period t, respectively. These represent the actual and day-ahead forecast values ​​of photovoltaic power output for microgrid i during time period t. e wt Expected value and standard deviation of (t,i); e pv Expected value and standard deviation of (t,i);

[0074] 1.3) Terminal multi-element load unit model, including:

[0075] Considering the basic energy needs of community microgrid users and combining the comprehensive response capabilities of diverse loads, the terminal diverse loads are divided into rigid loads and flexible loads: rigid loads are those that are not affected by incentive factors and guarantee users' basic production and living needs; flexible loads are those that are guided by compensation incentive mechanisms and are willing to sacrifice some energy experience for economic reasons. Based on the response method, flexible loads are further divided into vertical demand response loads and horizontal demand response loads. Considering user energy comfort, vertical demand response loads are only transferable loads; based on the "coupled response" characteristics between different energy needs of users, horizontal demand response refers to using different energy sources to meet the same production and living needs, manifested as energy substitution consumption.

[0076] The relevant model for vertical demand response is:

[0077]

[0078] In the formula: from top to bottom, the constraints are: load shift amount constraint, load response rate constraint, vertical shift binary variable constraint, and total load constant constraint. The load of microgrid i during time period t before and after demand response; These represent the vertical shifts of each load in microgrid i during time period t; These represent the vertical shift coefficients of each load demand response; , These represent the variables for shifting the load of microgrid i up and down by 0 and 1 during time period t; These represent the upper limits of the vertical translation rate for each load; the loads include three types: electrical, gas, and heat loads, i.e., X={P,G,H} and x={p,g,h} represent the electrical, gas, and heat loads respectively; for example: These represent the electricity, gas, and heat loads of microgrid i during time period t before demand response; These represent the electricity, gas, and heat loads of microgrid i in time period t after demand response; the same applies to other loads.

[0079] Lateral demand response models include:

[0080] Electricity-gas lateral demand response model:

[0081]

[0082] Electric-thermal cross-sectional demand response model:

[0083]

[0084] The above-mentioned electricity-gas and electricity-heat horizontal demand response models all include upper limit constraints on heterogeneous energy substitution and binary variable constraints on energy coupling; where: These represent the increase in electrical load due to the substitution of gas energy for electrical energy by microgrid i during time period t, and the decrease in electrical load due to the substitution of gas energy for electrical energy. These represent the increase in gas load due to gas energy replacing electrical energy in microgrid i during time period t, and the decrease in gas load due to gas energy being replaced by electrical energy. These represent the increase in electrical load due to the substitution of heat energy for electrical energy by microgrid i during time period t, and the decrease in electrical load due to the substitution of heat energy for electrical energy. λ represents the increase in heat load due to thermal energy replacing electrical energy in microgrid i during time period t, and the decrease in heat load due to thermal energy being replaced by electrical energy; p-g , λ g-p , λ p-h , λ h-p These represent the energy conversion substitution coefficients for electricity-gas, gas-electricity, electricity-heat, and heat-electricity, respectively. These represent the percentages of energy substitution for electricity-gas, gas-electricity, electricity-heat, and heat-electricity, respectively; μ p-g (t,i), μ g-p (t,i), μ p-h (t,i), μ h-p (t,i) represent the substitution of 0 and 1 variables for electricity-gas, gas-electricity, electricity-heat, and heat-electricity in microgrid i during time period t, respectively.

[0085] The comprehensive load equation constraint is:

[0086]

[0087] In the formula: These represent the electricity, gas, and heat loads of microgrid i during time period t before demand response; These represent the electricity, gas, and heat loads of microgrid i during time period t after demand response; These represent the upward shift of the electrical, gas, and heat loads of microgrid i during time period t; These represent the downward shift of the electricity, gas, and heat loads of microgrid i during time period t;

[0088] 1.4) The coupling device unit model is as follows:

[0089] The coupled equipment units within the community microgrid system include micro-turbines, electric chillers, heat absorption chillers, and gas chillers. The corresponding output and input power relationships are as follows:

[0090]

[0091] In the formula: V mt (t,i), P mt (t,i),H mt (t,i) represent the amount of natural gas consumed, the electrical power output, and the thermal power output of the micro-turbine within microgrid i during time period t, respectively; These represent the gas-to-electricity and gas-to-heat conversion efficiencies of the micro-turbine within the microgrid i during time period t; L gas The lower heating value of natural gas is taken as 9.7 kWh / m³. 3 ;P ec (t,i),H ac (t,i), G gc (t,i) represent the input power of the electric chiller, heat absorption chiller, and gas-fired chiller in microgrid i during time period t, respectively; C ec (t,i), C ac (t,i), C gc (t,i) represent the output power of the electric chiller, heat absorption chiller, and gas-fired chiller in microgrid i during time period t, respectively; These are the refrigeration efficiencies of electric chillers, heat absorption chillers, and gas-fired chillers, respectively.

[0092] 1.5) The energy storage device unit model is as follows:

[0093] The energy storage units within the community microgrid system include electrical, gas, thermal, and cold energy storage devices. The relevant constraints for their charging and discharging power are as follows:

[0094]

[0095] In the formula: S x (t,i) represent the energy storage status of each energy storage device in community microgrid i during time period t, and X represents the energy storage status of X. c (t,i) represent the charging power of each energy storage device in community microgrid i during time period t; X d (t,i) represent the energy release power of each energy storage device in community microgrid i during time period t; Δt represents the unit dispatch period. The loss rate of each energy storage device; These represent the charging efficiency, discharging efficiency, and rated capacity of each energy storage device, respectively. These represent the upper and lower limits of the energy storage status for each energy storage device; These are the upper limits of charging and discharging power for each energy storage device in the community microgrid i; These represent the 0 and 1 charge / discharge variables of each energy storage device within microgrid i during time period t; S x (0,i), S x (24,i) represent the initial and final energy storage states of each energy storage device in community microgrid i within a scheduling cycle, respectively; where the energy storage device unit in the community microgrid system includes electrical, gas, thermal, and cooling energy storage devices; that is, X={P,G,H,C} and x={p,g,h,c} represent the four types of energy: electrical, gas, thermal, and cooling, respectively; for example, S x In (t,i), x is p, then S p (t,i) represents the energy storage status of the energy storage device in community microgrid i during time period t, Sg (t,i) represent the energy storage status of the gas storage device in community microgrid i during time period t, and S represents the energy storage status of the gas storage device in community microgrid i during time period t. h (t,i) represent the energy storage status of the thermal energy storage device in community microgrid i during time period t, and S represents the energy storage status of the device. c (t,i) represent the energy storage status of the cold energy storage device in community microgrid i during time period t, and the others are similar;

[0096] Step S2 specifically involves: (as follows) Figure 3 As shown, the two-level game model considering economic benefits and carbon emission reduction benefits at different levels has the upper-level regional dispatch center as the game leader and the lower-level community microgrids as game followers. This one-leader-many-follower two-level interactive process can be described as follows: First, the upper-level regional dispatch center, based on the initial load curves of each community microgrid and considering the energy supply and demand relationship of electricity, gas, and heat between microgrids, determines the electricity, gas, and heat purchase energy of each community-level microgrid according to the principle of minimizing the total regional energy consumption cost, and formulates energy interaction and carbon trading schemes between microgrids, which are then distributed to the community microgrid data information management center; then, each lower-level community microgrid, based on its own load characteristics and the dispatch center's distribution plan, determines the energy purchase energy of each community-level microgrid according to the initial load curves of each community microgrid and considers the energy supply and demand relationship of electricity, gas, and heat between microgrids, and formulates energy interaction and carbon trading schemes between microgrids, which are then distributed to the community microgrid data information management center; then, each lower-level community microgrid, based on its own load characteristics and the distribution plan issued by the regional dispatch center, determines the energy purchase energy of each community microgrid according to the initial load curves of each community microgrid and considers the energy supply and demand relationship of electricity, gas, and heat between microgrids, and formulates energy interaction and carbon trading schemes between microgrids, which are then distributed to the community microgrid data information management center. The order, while meeting the diverse energy demands within the microgrid, determines the renewable energy output, operating conditions of energy coupling equipment, and comprehensive load demand response within the microgrid based on the principle of minimizing operating costs, and reports these to the regional dispatch center. The regional dispatch center, based on the data parameters reported by each community microgrid, adjusts the overall strategy for the region, re-formulates and issues energy purchase, energy exchange, and carbon trading instructions. This involves a secondary game interaction between the regional dispatch center and each community microgrid. This iterative process continues until both the upper and lower levels of interest achieve their optimal benefits, and a change in the strategy of any one player at any level does not cause a change in the strategy of the other player. The two-layer, one-master-many-follower game model is described as follows:

[0097]

[0098] In the game model G: Z is the set of game players, including the regional dispatch center RDC and each community microgrid CIES(i); S represents the set of strategies of the game players, and the set of operating strategies of the upper-level regional dispatch center Si. RDC Including various micro-online shopping energy X buy (i) Energy interaction quantity X between microgrids i and j i,j Carbon trading volume (CER) i,j Information such as the operational strategies of each lower-level community micronetwork (S) CIES(i) Including the power output of devices within the community microgrid. equipment (i) and the load after demand response X load (i) and other parameters; F is the payoff function of the game players, including the regional dispatch center payoff function f. RDC and the payment function f of each community micro-network CIES(i) .

[0099] Step S3 specifically involves: (e.g.) Figure 4 As shown, the initial total carbon emission allowance is determined by the carbon trading regulatory agency based on the region's quarterly historical energy consumption. Then, taking into account the historical carbon emission of each community microgrid in the region, the initial carbon emission allowance allocation scheme for regional users, determined according to the "total control and trading principles" in the quota-based carbon emission rights trading, serves the long-term low-carbon management of the multi-community microgrid system. Meanwhile, the carbon emission rights trading scheme between community microgrids, determined by the regional dispatch center through data obtained from the user's cycle energy consumption low-carbon optimization management and carbon emission accounting by the data information management center of each community microgrid, serves the short-term low-carbon management.

[0100] 3.1) The initial carbon emission quota model for regional users is as follows:

[0101]

[0102] In the formula: T is the total scheduling period; q represents the initial carbon emission allowance for community microgrid i; p q g q h q c These represent the carbon emission allowances per unit of electricity, gas, heat, and cooling load, respectively; P past (t,i), G past (t,i),H past (t,i), C past (t,i) represent the historical electricity, gas, heat, and cooling load data of community microgrid i during time period t;

[0103] 3.2) Carbon source analysis of community microgrid systems, such as Figure 5 As shown, the carbon emission accounting model is as follows:

[0104] Virtual carbon flows generated by coal-fired and gas-fired power plants enter community microgrid systems along with electrical and heat energy flows, resulting in indirect carbon emissions from the operation of electrical and heat equipment and electrical and heat loads. Therefore, electric chillers, heat absorption chillers, electrical loads, and heat loads are indirect carbon emission sources. Micro gas turbines, gas-fired chillers, and gas loads generate physical carbon emissions during operation and are considered direct carbon emission sources.

[0105] The direct carbon emission accounting model characterizing carbon emissions from direct carbon emission sources is as follows:

[0106]

[0107] In the formula: For the direct carbon emissions of the community microgrid during time period t; G mt (t,i) 、G gc (t,i) 、G L(t,i) represent the natural gas power consumed by the micro-turbine, gas chiller, and gas load in microgrid i during time period t, respectively; V mt (t,i) represents the volume of natural gas consumed by the micro-turbine within microgrid i during time period t; d g M represents the carbon emission factor for natural gas combustion. CO2 R and L represent the molar mass and molar volume constant of carbon dioxide, respectively; gas The lower heating value of natural gas is 9.7 kWh / m³. 3 ;

[0108] The indirect carbon emission accounting model characterizing the carbon emissions from indirect carbon emission sources is as follows:

[0109]

[0110] In the formula: The direct carbon emissions of the community microgrid at time t; d p (t) represents the carbon emission coefficient of electricity purchased from the main grid during time period t, which can be calculated based on the real-time composition of the main grid's electricity consumption; θ cfpp (t), θ ccpp (t) represents the output ratio of the coal-fired power plant and the carbon capture power plant at time t, respectively; σ is the carbon capture coefficient of the carbon capture power plant; d all This represents the carbon emission factor when all electricity purchased from the main grid is used for power generation by thermal power units, taking the marginal emission factor EF of electricity generated by the China regional power grid. OM With capacity marginal emission factor EF BM The arithmetic mean; d h The indirect carbon emission coefficient for thermal energy consumption; , P represents the proportion coefficients of purchased electricity and purchased heat energy of community microgrid i at time t; ec (t,i),H ac (t,i) represent the input power of the electric chiller and the heat absorption chiller in microgrid i during time period t, respectively; These represent the electricity and heat loads of microgrid i during time period t after demand response; P buy (t,i),H buy (t,i) represent the electrical and thermal energy of microgrid i in time period t, respectively; P wt (t,i), P pv (t,i) represent the actual wind and solar power outputs of microgrid i during time period t, respectively; P mt (t,i),H mt (t,i) represent the electrical power and thermal power output by the micro-turbine within microgrid i during time period t, respectively;

[0111] 3.3) Carbon emission trading methods for multi-community microgrid systems, such as Figure 6As shown, the corresponding model is as follows:

[0112] First, the carbon trading status of each community microgrid is determined: community microgrids with actual carbon emissions exceeding their initial carbon emission allowances are considered carbon trading buyers, while those with actual carbon emissions less than their initial allowances are considered carbon trading sellers. Then, based on the principle of minimizing carbon trading transaction costs, the regional dispatch center determines the carbon trading scheme between community microgrids to ensure full trading of carbon emission rights within the region. Finally, community microgrids with additional carbon emission right balances or shortfalls will further participate in the national carbon trading market. The relevant constraints for carbon trading are:

[0113]

[0114] In the formula: ACE(i) is the actual carbon emissions of microgrid i on a single scheduling day; This represents the initial carbon emission allowance for community microgrid i; This indicates the carbon emission rights balance or difference of community microgrid i; The regional dispatch center acts as an agent for the potential interaction between the community microgrid and the national carbon emission trading market; CER i,j This represents the carbon emission trading volume between community microgrids i and j.

[0115] Step S4 specifically involves:

[0116] 4.1) Objective function of daily operating cost of multi-community microgrid system consisting of energy purchase cost, energy interaction loss cost, and carbon trading transaction cost. for:

[0117]

[0118] In the formula: These are the total energy purchase cost, the cost of energy exchange losses between community microgrids, and the transaction cost of carbon emission trading; T, N m These represent the total number of scheduling periods and the number of community microgrids within the region, respectively; p p (t), p g (t), p h (t) represents the price of electricity, gas, and heat energy during time period t, respectively; P buy (t,i), G buy (t,i),H buy (t,i) represent the electricity, gas, and heat energy of microgrid i in time period t, respectively; P i,j (t), G i,j (t) represent the electrical and gas energy interaction quantities between microgrid i and microgrid j, respectively; , a and b are the energy loss coefficients for electricity and gas interaction between community microgrids, respectively; a and b are the service cost coefficients for carbon trading between community microgrids and the national carbon trading market, and between community microgrids, respectively. To act as an agent for the regional dispatch center to handle the potential interaction between the community microgrid and the national carbon emission trading market; This indicates the carbon emission trading volume between community microgrids i and j;

[0119] 4.2) The energy purchase constraint is:

[0120]

[0121] In the formula: These are the upper limits for electricity, gas, and heating energy in the community microgrid i;

[0122] 4.3) The energy interaction constraints between community microgrids are:

[0123]

[0124] In the formula: These represent the upper limits of electricity and gas energy interaction between community microgrids i and j, respectively.

[0125] 4.4) The energy supply and demand margin constraint of the community microgrid is:

[0126]

[0127] Where: ESDM x (t,i) represents the ratio of the demand for energy type x to the maximum supply in community microgrid i during time period t; Indicates ESDM x The upper limit; and Let x represent the actual total demand and maximum total supply of energy type x within microgrid i during time period t; and These represent the actual demand and maximum supply of energy type x for device unit k in community microgrid i during time period t; This represents the energy purchased by energy type x within microgrid i during time period t; Energy x interaction quantity between microgrids i and j (from j to i).

[0128] Step S5 specifically involves:

[0129] 5.1) Energy purchase cost Equipment operation and maintenance costs Comprehensive demand response compensation cost Energy interaction benefits Carbon trading revenue Objective function of daily operating cost of the community microgrid system for:

[0130]

[0131] In the formula: These are the operation and maintenance cost coefficients for each device in the community microgrid; s, γ s N s These represent the scene number, the probability of the corresponding scene, and the total set of scenes, respectively; P pv (t,i,s),P wt (t,i,s) represent the power of photovoltaic and wind power of microgrid i in scenario s during time period t, respectively; P c (t,i,s),G c (t,i,s),H c (t,i,s),C c (t,i,s) represent the electrical, gas, heat, and cold energy storage charging power within microgrid i in scenario s during time period t, respectively; P d (t,i,s),G d (t,i,s),H d (t,i,s),C d (t,i,s) represent the electrical, gas, thermal, and cold energy storage discharge power within microgrid i in scenario s during time period t, respectively; α p (i), α g (i), α h (i) represent the longitudinal demand response compensation coefficients for electricity, gas, and heat loads of microgrid i, respectively; β e,g (i), β e,h (i) represent the horizontal demand response compensation coefficients for electricity-gas and electricity-heat of microgrid i, respectively; These represent the electricity and gas energy exchange prices between community microgrids during time period t; p CER Indicates the market price of carbon emission rights; Gp-g L(t,i) represents the amount of electrical load reduction caused by the replacement of electrical energy with gas energy in microgrid i during time period t; Gp-g L(t,i) represents the amount of gas load reduction caused by the replacement of gas energy with electrical energy in microgrid i during time period t. The amount of electrical load reduction caused by the replacement of electrical energy with heat energy in microgrid i during time period t; These represent the reduction in heat load caused by the replacement of thermal energy with electrical energy in microgrid i during time period t;

[0132] 5.2) The equipment output constraint is:

[0133]

[0134] In the formula: These represent the upper limits of photovoltaic and wind power output of the community microgrid i at time t; , These are the upper limits of input power for the micro gas turbine, electric chiller, heat absorption chiller, and gas chiller of the community microgrid i, respectively.

[0135] 5.3) The energy balance constraint of the community microgrid is:

[0136]

[0137] Step S6 specifically involves:

[0138] The master-slave game optimization model for a multi-community microgrid system considering energy interaction and carbon trading is expressed as follows:

[0139]

[0140] In the formula: These represent the load and equipment output after the optimal operating decision of the lower-level community microgrid (game follower); These represent the energy purchased by the community microgrid, the energy interaction between community microgrids, and the carbon trading volume between community microgrids when the upper-level regional dispatch center (the game leader) issues the optimal dispatch instruction. The carbon trading-related constraints mentioned above correspond to the model in step S3, and the comprehensive demand response constraints correspond to the terminal multi-load unit model in S1.

[0141] Step S7 specifically involves: (e.g.) Figure 7 The solution method shown is as follows: The upper-level particle swarm optimization algorithm is nested with the lower-level Gurobi solver.

[0142] 7.1) Input the initial data information of each community microgrid, including wind and solar power forecast data, comprehensive load forecast data, real-time energy prices, operating parameters of energy coupling equipment, and historical carbon emission data;

[0143] 7.2) Initialize the particle swarm This includes setting the particle population size N and setting the maximum number of iterations k. max Initialize particle movement speed and initialize iteration count k=0, where These represent the energy consumption of the first-generation community micro-grid, the energy interaction between community micro-grids, and the carbon trading volume between community micro-grids, respectively.

[0144] 7.3) The regional dispatch center will issue dispatch instruction X to the community micro-network;

[0145] 7.4) Each lower-level community microgrid receives the scheduling instructions and uses the Gurobi solver to solve the system decision model established in step S5. In the formula This represents the load and equipment output after the demand response in the kth generation;

[0146] 7.5) The regional dispatch center receives optimization decision information uploaded by each community microgrid. The fitness of each particle is calculated based on the optimal decision model established in step S4, and the particle population is updated. Let the number of iterations be k = k + 1;

[0147] 7.6) Determine whether the two-layer game optimization model has reached the game equilibrium point. If it satisfies... This indicates that a game equilibrium solution has been found. Otherwise, proceed to 7.3) to continue iteratively solving, where... For a very small margin;

[0148] 7.7) Determine if the maximum number of iterations has been reached. If k < k max (Refer to 7.3), if k≥k max Stop the iteration and output the optimization results.

[0149] The following are specific examples:

[0150] 1. Parameter settings

[0151] This invention focuses on multi-functional urban areas integrating industry, commerce, and residence. It employs Monte Carlo sampling to simulate 1000 random wind and solar power output scenarios for each community microgrid. Then, based on k-means clustering, these scenarios are reduced to four typical output scenarios. The output curves for each typical scenario and their corresponding probabilities are shown below. Figure 8 As shown. The electricity, gas, and heat purchase prices involved in the invention model, as well as the electricity and gas energy exchange prices between community microgrids, are as follows: Figure 9 As shown.

[0152] 2. Simulation Results and Comparative Analysis

[0153] To verify the beneficial effects of this invention, five scenarios were set up for comparative analysis. The specific settings of the scenarios are as follows:

[0154] Scenario 1: Without considering energy interaction and carbon trading between community microgrids, or demand-side management within the community microgrids, each community microgrid optimizes independently, and the regional dispatch center only provides energy supply and carbon emission trading services to each community microgrid;

[0155] Scenario 2: Considering energy interaction and carbon trading between community microgrids, with centralized optimization by the regional dispatch center, each community microgrid does not conduct demand-side management, but only operates according to the dispatch instructions issued by the regional dispatch center, and does not have autonomy;

[0156] Scenario 3: Without considering energy interaction and carbon trading between community microgrids, each community microgrid optimizes independently based on demand-side management, and the regional dispatch center only provides energy supply and carbon emission trading services to each community microgrid;

[0157] Scenario 4: Considering the energy interaction between community microgrids and the demand-side management within the community microgrids, while the carbon emission trading of each community microgrid is handled by the regional dispatch center, the daily operation of the urban area is optimized and dispatched using the one-master-many-slave game optimization model established in this paper.

[0158] Scenario 5: Considering energy interaction and carbon emission trading among community microgrids, as well as demand-side management within the community microgrids, the daily operation of the urban area is optimized and scheduled using the master-slave game optimization model established in this paper.

[0159] 1) Analysis of economic and low-carbon benefits

[0160] Table 1 Operating Costs and Carbon Emissions in Different Scenarios

[0161]

[0162] Analysis of Table 1 shows that the simulation results for Scenario 2 show a decrease of ¥518 in the total regional operating cost compared to Scenario 1. This is mainly reflected in the reduction of regional energy purchase costs and community microgrid operation and maintenance costs. The reason for this is that the RDC centralized optimization method, which considers energy interaction and carbon trading between community microgrids, compared to individual optimization by each CIES at the community level, can formulate differentiated energy purchase plans based on the comprehensive energy supply and demand characteristics of different community microgrids, guide relatively independent community microgrids to participate in energy interaction between CIES, and balance the energy demand of different community microgrids within the region. The simulation results for Scenario 3, compared to Scenario 1, show that while the user-side demand response compensation cost increased by ¥263 due to the adoption of demand-side management methods based on independent optimization by each community microgrid, the vertical demand response method shifts energy demand from peak electricity price periods to off-peak periods, and the horizontal demand response method enables substitution consumption between energy sources with different prices. This significantly reduces regional energy purchase costs, resulting in a decrease of ¥1078 in the total system operating cost. Comparing the simulation results of Scenario 4 with those of Scenarios 1, 2, and 3, the total operating cost of the system decreased by ¥1529, ¥1011, and ¥451, respectively. While the cost of energy interaction increased, the proposed master-slave game-theoretic optimization model further reduced the regional energy purchase cost and the community microgrid operation and maintenance cost by optimizing the daily operation and scheduling of the urban area. This demonstrates the significant economic benefits of the model and scheduling method. Building upon Scenario 4, Scenario 5 further considers carbon trading among community microgrids. By introducing carbon emission rights trading, the transaction costs of participating in the carbon trading market are reduced while maintaining a relatively constant total regional carbon emissions. Therefore, the master-slave game-theoretic dual-layer optimization scheduling method for multi-community microgrid systems, which considers energy interaction and carbon trading, proposed in this invention prioritizes minimizing the total operating cost at the regional dispatch center. Each community microgrid adjusts the output of its internal energy equipment based on its energy purchase, energy interaction, and carbon trading instructions, further optimizing its own operating costs on the basis of centralized scheduling, and possessing considerable autonomy in decision-making.

[0163] To further demonstrate the beneficial effects of this invention, based on scenario 5 and taking a residential community (CIES1) as an example, we will conduct a comprehensive analysis of demand response, energy supply and demand balance, energy interaction, and carbon trading:

[0164] 2) Comprehensive Demand Response Analysis

[0165] according to Figure 10 Analyze the vertical demand response: Figure 10 In (a), the electricity load demand during the periods of 12:00-14:00 and 18:00-22:00 shifts to the period of 23:00-07:00, which aligns with... Figure 9As shown in the peak-valley electricity price trend, it can be seen that the energy cost reduction of community microgrids through peak shaving and valley filling via vertical demand response is greater than the carbon trading costs indirectly generated by the increase in actual carbon emissions due to adjustments in electricity consumption behavior. Figure 10 In (c), the heat load demand from 24:00 to 06:00 shifts to other time periods. The reason for this is that the initial heat load demand of residential communities during this period exceeds the 400kW transmission limit of the heating network, and purchasing heat solely from the upper-level heating network cannot meet their basic heat load demand. This demonstrates that by adjusting the energy consumption habits of load-side users through vertical demand response, the reliability of the system's energy supply can be improved, while also delaying or even avoiding the expansion of energy supply infrastructure due to increased load demand.

[0166] according to Figure 10 An analysis of the horizontal demand response of residential communities: by Figure 10 As shown in (b) and (c), from 08:00 to 22:00, guided by the horizontal demand response model, residential community users reduced their electricity consumption by substituting gas and heat energy. This demonstrates that achieving substitution between heterogeneous energy sources through the user-side horizontal demand response model not only reduces system energy purchase costs but also controls system carbon emissions, improving the low-carbon economic benefits of the multi-community integrated energy system and enhancing its core competitiveness in the context of "dual carbon" (carbon dioxide, carbon emissions, and carbon sequestration).

[0167] 3) Energy supply and demand balance analysis

[0168] according to Figure 11 An analysis of the energy supply and demand balance in residential communities: Figure 11 (a) It can be seen that during off-peak electricity prices (23:00-07:00), residential communities purchase large amounts of electricity from the main grid to meet their basic electricity load needs, while simultaneously using electric chillers for energy conversion to meet some of their cooling load needs. They also sell surplus electricity to other community microgrids to earn revenue. Furthermore, based on the storage and release characteristics of electric energy storage, it is stored during periods of lower electricity prices (06:00-07:00, 15:00-16:00) and released during periods of higher electricity prices (13:00-14:00, 20:00-22:00), achieving "low-price storage, high-price release." During peak electricity prices (12:00-22:00), considering that gas prices are much lower than electricity prices, using micro gas turbines for gas-to-electricity conversion can further reduce the system's energy purchase costs. Figure 11 (b) It can be seen that, in addition to purchasing gas from the superior gas network to meet the basic gas load demand within the residential community, a large amount of additional gas is purchased during peak electricity price periods (12:00-14:00, 16:00-22:00) to supply micro-turbines for gas-to-electricity conversion, thereby reducing the overall energy purchase cost. It is also noted that at 22:00, the purchased gas volume exceeds the gas network transmission limit (500kW), necessitating the purchase of gas from other community micro-networks to meet the gas consumption demand during that period. Figure 11(c) It can be seen that throughout the entire dispatch cycle, residential communities mainly purchase heat from the upper-level heating network to meet their basic heat load needs. Only during a few periods (12:00-14:00, 16:00-22:00) do they utilize micro-turbine cogeneration to meet the electricity demand within the microgrid and also supply a portion of the heat load demand. During the period from 08:00 to 22:00, considering factors such as energy prices, a portion of the cooling load demand is supplied through heat absorption chillers. Figure 11 (d) It can be seen that during the off-peak hours of electricity price (23:00-07:00), the cooling load demand in the microgrid is fully met by electric chillers, while cold storage equipment is used for "low-end storage and high-end release". As the electricity price increases, the supply ratio of electric chillers gradually decreases. During the peak hours of electricity price (12:00-14:00, 19:00-22:00), most of the cooling load is met by heat absorption chillers.

[0169] Based on the above analysis, the multi-community microgrid system dual-layer optimization scheduling method with one master and many slaves, which takes into account energy interaction and carbon trading, proposed in this invention, allows each microgrid operator at the community level to fully leverage the synergistic advantages of energy supply, conversion, and storage equipment within the community microgrid, according to the actual load after responding to user demand and changes in main grid prices. Through multi-energy coupling, it reduces the overall energy consumption cost of the system, improves the reliability of energy supply and the flexibility of supply and demand, and achieves a balance between supply and demand in the comprehensive energy system of each community microgrid.

[0170] 4) Energy Interaction and Carbon Trading Analysis

[0171] according to Figure 12 (a) Analysis of energy interaction between community microgrids: During the period from 23:00 to 07:00, both the residential community (CIES1) and the commercial district (CIES3) sell electricity to the industrial park (CIES2). This is because CIES2 has a larger load base during this period, exceeding the transmission limit of the upper-level power grid, while CIES1 and CIES2 have smaller loads and greater capacity to purchase energy. Energy interaction between community microgrids better meets the electricity needs of users within the microgrids. During the periods of 11:00 and 15:00-18:00, CIES2 also sells electricity to CIES3 for the same reason. Regarding gas energy interaction between community microgrids, CIES2 and CIES3 only sell gas energy to CIES1 during the period from 21:00 to 22:00.

[0172] according to Figure 12(b) Analysis of carbon trading among community microgrids: Based on energy supply and demand characteristics, the carbon emission rights balances or deficits obtained by each community microgrid under the lower-level low-carbon economic model optimization vary significantly. CIES1 has a carbon emission rights balance of 689.47 kg, while CIES2 has a carbon emission rights deficit of 788.89 kg. Under the upper-level model optimization, CIES2 purchases 608.54 kg and 180.35 kg of carbon emission rights from CIES1 and CIES3 respectively to make up for its carbon emission rights deficit. Based on the full trading of carbon emission rights within the region, CIES3 then sells 80.93 kg of carbon emission rights to the external carbon trading market through the regional dispatch center.

[0173] Therefore, based on the master-slave game-theoretic dual-layer optimization scheduling method for multi-community microgrid systems that takes into account energy interaction and carbon trading proposed in this invention, the regional dispatch center can issue energy interaction and carbon trading instructions according to the real-time energy demand and actual carbon emissions of each microgrid at the community level. This fully leverages the advantages of the regional multi-community integrated energy system in resource coordination and energy mutual assistance. It not only ensures the energy supply and demand balance of the microgrids at the community level but also promotes the multi-energy supply and demand balance at the regional level. Furthermore, it optimizes the overall low-carbon economic benefits of the region while taking into account the rights and interests of each microgrid at the community level.

[0174] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for multi-community microgrid system master-slave game double-layer optimization scheduling, characterized in that, The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. The application relates to a community micro-grid system network architecture and model, a multi-community micro-grid system one-master multi-slave game double-layer model, a multi-community micro-grid system low-carbon management framework and model, an upper-layer regional dispatching center optimization decision model, a lower-layer community micro-grid system operation decision model, a multi-community micro-grid system one-master multi-slave game optimization model, and an optimization algorithm for solving the one-master multi-slave game optimization model. ​ In the game double-layer model G: Z is the set of game subjects, including regional dispatch center RDC and each community microgrid CIES(i); S represents the strategy set of game subjects, the operation strategy set S of upper layer regional dispatch center RDC including the energy purchase of each microgrid x buy (i), the energy interaction amount x between microgrids i and j i,j , the carbon trading amount CER i,j information, the operation strategy set S of each community microgrid in lower layer CIES(i) including the output of equipment in community microgrid i x equipment (i) and the load amount x after demand response load (i) parameter; F is the payment function of game subjects, including the payment function f of regional dispatch center RDC and the payment function f of each community microgrid CIES(i) ; ​ ​ ​ ​ In the formula: respectively represent the energy purchased by the community microgrid, the energy interaction between microgrids, and the carbon trading volume, respectively represent the post-response load and the equipment output of the lower community microgrid when the optimal operation decision is made; respectively represent the equipment output and the post-response load of the community microgrid i, respectively represent the energy purchased by the community microgrid, the energy interaction between microgrids, and the carbon trading volume when the optimal dispatch instruction is issued by the upper regional dispatch center.

2. The multi-community microgrid system one master multi-slave game double- layer optimization scheduling method according to claim 1, characterized in that, The multi-community microgrid system daily operation cost objective function f is composed of the energy purchasing cost, the energy interaction loss cost, and the carbon emission right transaction procedure cost RDC is: In the formula: respectively are the total energy purchasing cost, the energy exchange loss cost between community microgrids, and the carbon emission trading procedure cost; T, N m respectively are the total scheduling period number and the number of community microgrids in the region; respectively represent the prices of electricity, gas, and heat energy at the t period; respectively are the electricity, gas, and heat energy purchased by the microgrid i at the t period; respectively represent the electricity and gas energy exchange amount between the microgrid i and the microgrid j; respectively are the electricity and gas energy exchange loss coefficients between community microgrids; a and b are respectively the service cost coefficients of the carbon trading between the community microgrid and the national carbon trading market and the community microgrid; is the potential exchange amount between the community microgrid i and the national carbon emission trading market; represents the carbon emission trading amount between the community microgrids i and j.

3. The multi-community microgrid system one master multi-slave game double- layer optimization scheduling method according to claim 1, characterized in that, Energy purchase cost Equipment operation and maintenance costs Comprehensive demand response compensation cost Energy interaction benefits Carbon trading revenue Objective function of daily operating cost of the community microgrid for: In the formula: respectively represent the price of electricity, gas, and heat energy at time period t; respectively represent the electricity, gas, and heat energy purchased by microgrid i at time period t; respectively represent the scenario number, corresponding scenario probability, and total scenario set; respectively represent the operation and maintenance cost coefficients of each device in the community microgrid; respectively represent the photovoltaic and wind power of microgrid i at time period t in scenario s; respectively represent the natural gas power consumed by the micro-turbine and gas-driven refrigerator in microgrid i at time period t; respectively represent the input power of the electric refrigerator and heat absorption refrigerator in microgrid i at time period t; respectively represent the charging and discharging power of the electric, gas, heat, and cold energy storage in microgrid i at time period t in scenario s; respectively represent the vertical demand response compensation coefficients of the electricity, gas, and heat load of microgrid i; respectively represent the horizontal demand response compensation coefficients of the electricity-gas and electricity-heat of microgrid i; respectively represent the upward shift amount of the electricity, gas, and heat load of microgrid i at time period t; respectively represent the downward shift amount of the electricity, gas, and heat load of microgrid i at time period t; is the reduced electricity load of microgrid i at time period t due to the replacement of electricity energy by gas energy; is the reduced gas load of microgrid i at time period t due to the replacement of gas energy by electricity energy; is the reduced electricity load of microgrid i at time period t due to the replacement of electricity energy by heat energy; respectively represent the reduced heat load of microgrid i at time period t due to the replacement of heat energy by electricity energy; respectively represent the electricity and gas energy interaction price between community microgrids at time period t; respectively represent the electricity and gas energy interaction amount between microgrid i and microgrid j; represents the market carbon emission right trading price; is the potential interaction amount between community microgrid i and the national carbon emission right trading market; represents the carbon emission right trading amount between community microgrids i and j.

4. The multi-community microgrid system one master multi-slave game double- layer optimization scheduling method according to claim 1, characterized in that, ​

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