A Multi-Agent MEMG Scheduling Method Based on Distributionally Robust Optimization

Abstract

The present invention provides a multi-agent MEMG scheduling method based on distributed robust optimization, the method comprising: constructing a multi-agent MEMG-prosumer operation model with MEMG as the upper layer and prosumers as the lower layer; constructing an objective function minCoM for a single-agent MEMGi based on minimizing its own operation cost; MGi ; The minCoM MGi represents the minimum value of the total operating cost of MEMGi; optimizes the multiple uncertain subjects in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere; constructs the objective function of the multi-agent MEMG microgrid alliance based on the Nash negotiation theory; constructs the objective function PR for the prosumer J within MEMGi based on its own minimization operation cost model i,J , the PR i,J represents the total cost of the Jth prosumer within the MEMGi; the MEMG-prosumer model is equivalently converted and solved. This invention improves the overall operational economics of multi-agent MEMGs through cooperative game-playing.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a multi-agent MEMG dispatching method based on distributed robust optimization. Background Art

[0002] Distributed renewable energy (DRG) boasts broad development prospects, including short construction cycles, diverse application scenarios, low environmental impact, and advanced technological maturity. However, the randomness, intermittency, and volatility of its output pose significant risks to the safe operation of the power grid.

[0003] Multi-energy microgrid (MEMG) can integrate different types of distributed energy in one area, providing users with the supply of multiple energy sources such as cooling, heating, and electricity, and realizing the complementary application of multiple energy sources, energy cascade and recycling.

[0004] In the future, the proportion of distributed resources in MEMG will further increase, and the form of "interconnection between networks" can effectively improve the economy, stability and distributed resource absorption level of the multi-agent MEMG system. However, during its operation, it will inevitably be affected by multiple uncertain subjects such as renewable energy and purchase / sale electricity prices, and the uncertainty factors will be further enhanced. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a multi-agent MEMG scheduling method based on distributed robust optimization. By constructing a multi-agent MEMG-producer-consumer operation model, multiple uncertain subjects in the model are optimized, and the impact of multiple uncertain subjects on system optimization is fully considered, thereby improving the robustness of the system and the economy of the overall operation.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a multi-agent MEMG scheduling method based on distributed robust optimization, the method comprising:

[0007] Construct a multi-agent MEMG-prosumer operation model with MEMG at the upper layer and prosumers at the lower layer;

[0008] For a single MEMGi, the objective function minCoM is constructed based on its own minimization of operating cost. MGi ; The minCoM MGi Represents the minimum value of the total operating cost of MEMGi;

[0009] Optimizing multiple uncertain agents in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere;

[0010] According to Nash negotiation theory, the objective function of the multi-agent MEMG microgrid alliance is constructed;

[0011] For the internal prosumer J of MEMGi, the objective function PR is constructed based on its own minimization operation cost model. i,J , the PR i,J represents the total cost of the J-th prosumer within the MEMGi;

[0012] The MEMG-prosumer model is solved after equivalent transformation.

[0013] On the MEMG side, the amount of electricity, natural gas, power consumption of electrical equipment and charge / discharge power of energy storage equipment interacting between the entities are collected through metering equipment. The objective function minCoM MGi The expression is:

[0014]

[0015] In formula (1), minCoM MGi represents the minimum value of the total operating cost of MEMGi, Represents the profit of MEMG from selling electricity to prosumers, Represents the profit of P2P interaction between MEMGs, Represents the electricity purchase cost of MEMGi and the upper grid, Represents the profit from electricity sales, represents the cost of the internal gas turbine equipment, Represents the cost of electric boiler equipment, Represents the cost of energy storage equipment, μ g Represents the unit natural gas purchase cost, c m represents the unit operation and maintenance cost of the electric boiler, c E represents the unit deterioration cost of energy storage, represents the natural gas purchase amount of the gas turbine at time t, represents the power consumption of the electric boiler at time t, and Respectively represent the charging / discharging power of energy storage at time t;

[0016] In formula (2), On behalf of MEMGi, the electricity price set for the Jth prosumer is determined. represents the electricity purchasing strategy decided by prosumer J based on the electricity price set by MEMGi, and m is the number of prosumers within MEMGi;

[0017] In formula (3), represents the interactive electricity price of MEMGi and MEMGj at time t, and Represents the energy interaction between the two at time t; when If it is greater than 0, it means that MEMGi sells electricity to MEMGj; otherwise, it means that MEMGi buys electricity from MEMGj;

[0018] Formulas (4) and (5) represent the expected value of the electricity purchase cost / electricity sales profit of MEMGi and the upper power grid under the worst distribution of electricity purchase / sale prices, respectively. and represent the random variables of the purchase / sale electricity price of MEMGi and the upper power grid, and They represent the amount of electricity purchased / sold at time t respectively.

[0019] The optimization of multiple uncertain agents in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere includes:

[0020] The distribution set of electricity purchase / sale prices is represented by the fuzzy set based on the Wasserstein ball:

[0021]

[0022] In formulas (6) and (7), and They represent the empirical distribution of the purchase / sale electricity price at time t, B(Ξ) and S(Ξ) represent the set of probability distributions of the purchase / sale electricity price, and P represents the distribution within the set whose distance from the empirical distribution is less than or equal to ε.

[0023] The optimization of multiple uncertain agents in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere further includes:

[0024] The uncertainty set of new energy is constructed based on the Wasserstein sphere, and the corresponding expression is:

[0025]

[0026] In formula (8), represents the empirical distribution of renewable energy within MEMGi, R(Ξ) represents the support set of renewable energy, ε i,res is the Wasserstein sphere radius of MEMGi renewable energy.

[0027] According to the Nash negotiation theory, the objective function of the multi-agent MEMG microgrid alliance is constructed, including:

[0028]

[0029] In formula (9), Represents the maximum benefit of multi-agent MEMG, and the constraints are described Represents the maximum profit of MEMGi when it is run independently, the U iRepresents the interests of MEMGi after participating in the cooperation, and minCoM MGi The optimal solutions are opposite to each other, and the constraints This means that the total revenue of any MEMGi after participating in the cooperation is not less than the revenue before the cooperation.

[0030] The objective function PR is constructed for the internal prosumer J of MEMGi based on its own minimization operation cost model. i,J ,include:

[0031]

[0032] In formula (10), PR i,J represents the total cost of the J-th prosumer within MEMGi, represents the cost of electricity purchased by prosumer J from MEMGi, represents the load shifting cost of prosumer J participating in demand response, represents the load interruption cost of prosumer J itself participating in demand response, c1 and c2 represent the unit shifting cost and unit load shedding cost respectively.

[0033] The equivalent conversion and solution of the MEMG-prosumer model includes:

[0034] Based on the duality theory, Equation (4-5) is equivalently transformed into:

[0035]

[0036] In formulas (11), (12), and (13), and is the Lagrange multiplier, N pb and N ps are the total number of samples corresponding to the purchase / sale electricity price at time t, and is the purchase / sale electricity price corresponding to the I-th sample at time t.

[0037] The equivalent conversion and solution of the MEMG-prosumer model further includes:

[0038] Based on the mean inequality, Equation (9) is equivalently transformed into:

[0039]

[0040] Based on ADMM combined with CCG algorithm, equations (14) and (15) are solved in a distributed manner.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a multi-agent MEMG scheduling method based on distributed robust optimization. It constructs a multi-agent MEMG-prosumer operation model with MEMG as the upper layer and prosumers as the lower layer. Based on distributed robust optimization (DRO), multiple uncertain subjects in the model are optimized, and the optimization model of the upper-layer MEMG and the optimization model of the lower-layer prosumers are equivalently converted and solved. The multi-agent MEMG-prosumer operation model provides a more economical scheduling solution for each MEMG, thereby obtaining better economic benefits, improving the level of new energy consumption within the system, avoiding the impact of excessive electricity sales on the upper power grid, and facilitating the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 This is a multi-agent MEMG interaction model diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The principles and features of the present invention are described below with reference to the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0046] Example 1

[0047] Figure 1 A multi-agent MEMG interaction model diagram provided in the first embodiment of the present invention is shown as follows: Figure 1 As shown in the figure, in the model, multi-energy microgrids not only exchange power with each other but also conduct complex power exchanges with the upper-level power grid. The internal structure of a multi-energy microgrid is complex. The simplified multi-energy microgrid mainly includes its own power grid, gas turbines, wind power, and photovoltaic power.

[0048] An embodiment of the present invention provides a multi-agent MEMG scheduling method based on distributed robust optimization, including:

[0049] Construct a multi-agent MEMG-prosumer operation model with MEMG as the upper layer and prosumers as the lower layer.

[0050] First, for a single MEMGi, the objective function minCoM is constructed based on its own minimization of operating cost MGi ; The minCoM MGi Represents the minimum value of the total operating cost of MEMGi.

[0051] Furthermore, on the MEMG side, the amount of electricity, natural gas, power consumption of electrical equipment, and charge / discharge power of energy storage equipment interacting between various entities are collected through metering equipment. The objective function minCoM MGi The expression is:

[0052]

[0053]

[0054] In formula (1), minCoM MGi represents the minimum value of the total operating cost of MEMGi, Represents the profit of MEMG from selling electricity to prosumers, Represents the profit of P2P interaction between MEMGs, Represents the electricity purchase cost of MEMGi and the upper grid, Represents the profit from electricity sales, represents the cost of the internal gas turbine equipment, Represents the cost of electric boiler equipment, Represents the cost of energy storage equipment, μ g Represents the unit natural gas purchase cost, c m represents the unit operation and maintenance cost of the electric boiler, c E represents the unit deterioration cost of energy storage, represents the natural gas purchase amount of the gas turbine at time t, represents the power consumption of the electric boiler at time t, and Respectively represent the charging / discharging power of energy storage at time t;

[0055] In formula (2), On behalf of MEMGi, the electricity price set for the Jth prosumer is determined. represents the electricity purchasing strategy decided by prosumer J based on the electricity price set by MEMGi, and m is the number of prosumers within MEMGi;

[0056] In formula (3), represents the interactive electricity price of MEMGi and MEMGj at time t, and Represents the energy interaction between the two at time t; when If it is greater than 0, it means that MEMGi sells electricity to MEMGj; otherwise, it means that MEMGi buys electricity from MEMGj;

[0057] Formulas (4) and (5) represent the expected value of the electricity purchase cost / electricity sales profit of MEMGi and the upper power grid under the worst distribution of electricity purchase / sale prices, respectively. and represent the random variables of the purchase / sale electricity price of MEMGi and the upper power grid, and They represent the amount of electricity purchased / sold at time t respectively.

[0058] Furthermore, the constraints of the MEMGi model include:

[0059]

[0060] Formulas (1-1), (1-2), and (1-3) represent the charge / discharge logic constraints and the upper and lower limit constraints of the internal energy storage of MEMGi, where and is the charge / discharge 0-1 variable of the energy storage, and represents the charging / discharging power of the energy storage at time t, and Respectively represent the maximum charge / discharge capacity;

[0061] Furthermore, the constraints of the MEMGi model also include:

[0062]

[0063] Formulas (1-4), (1-5), (1-6), and (1-7) represent the logical relationship between the start and stop of the gas turbine and the operating status within MEMGi, where and Both are 0-1 variables, representing the start and stop and operation logic variables of the gas turbine respectively.

[0064] Furthermore, the constraints of the MEMGi model also include:

[0065]

[0066] Formulas (1-8) and (1-9) represent the upper and lower limits of the electricity purchased and sold by MEMGi and the upper-level power grid, where and Represent the maximum purchase / sale power of MEMGi and the upper power grid respectively; It should be noted that as long as the average purchase price of MEMGi and the upper power grid at time t is greater than the average sales price of electricity, even if equations (1-8) and (1-9) do not introduce 0-1 variables similar to equations (1-2) and (1-3), MEMGi will not purchase or sell electricity at the same time as the upper power grid;

[0067] Furthermore, the constraints of the MEMGi model also include:

[0068]

[0069] Formulas (1-10), (1-11), and (1-12) represent the relationship between the energy storage state of charge and the charge and discharge capacity, as well as the upper and lower limits of the state of charge, where represents the state of charge of the energy storage at time t, Represents the initial capacity of energy storage, SOC max and SOC min Respectively represent the upper and lower limits of the energy storage charge state, η ch and η dis Respectively represent the charging / discharging efficiency of energy storage;

[0070] Furthermore, the constraints of the MEMGi model also include:

[0071]

[0072] Formulas (1-13) and (1-14) represent the gas purchase volume range of the gas turbine and the output range of the electric boiler, respectively. represents the natural gas purchase amount of the gas turbine at time t, and Indicates the upper and lower limits of gas purchase when the gas turbine is in operation. represents the power consumption of the electric boiler at time t, Indicates the upper limit of electricity consumption of electric boiler;

[0073] Furthermore, the constraints of the MEMGi model also include:

[0074]

[0075] Equations (1-15) and (1-16) represent the energy interaction between microgrids i and j, where Represents the upper limit of the power interaction between two MEMGs;

[0076] Furthermore, the constraints of the MEMGi model also include:

[0077]

[0078] Formula (1-17) represents the interactive electricity price range between the upper-level MEMGi and the lower-level prosumers, where and Indicates the upper and lower limits of the electricity price at time t;

[0079] Furthermore, the constraints of the MEMGi model also include:

[0080]

[0081] Equations (1-18) and (1-19) represent the electrical power and thermal power balance constraints within MEMGi, respectively. and represents the size of renewable energy, electric load, and thermal load at time t, η g ,η h and η b They represent the electricity production efficiency, heat production efficiency of the gas turbine and the heat production efficiency of the electric boiler respectively.

[0082] Preferably, multiple uncertain agents in the multi-agent MEMG-prosumer operation model are optimized based on the Wasserstein sphere.

[0083] Furthermore, the Wasserstein sphere is used to optimize the multiple uncertain agents in the multi-agent MEMG-prosumer operation model, including:

[0084] The distribution set of electricity purchase / sale prices is represented by the fuzzy set based on the Wasserstein ball:

[0085]

[0086] In formulas (6) and (7), and They represent the empirical distribution of the purchase / sale electricity price at time t, B(Ξ) and S(Ξ) represent the set of probability distributions of the purchase / sale electricity price, and P represents the distribution within the set whose distance from the empirical distribution is less than or equal to ε.

[0087] Furthermore, the fuzzy set of electricity purchase / sale price and It can be expressed as a set of distributions with the empirical distribution as the center and the distance from the empirical distribution less than or equal to ε. The mathematical expression of the distance between the two distributions is:

[0088]

[0089] In formula (2-1), ||ξ k -ξ I || p Represents the p-norm, which is usually taken as 1 in calculations. K and N represent the distribution P and The total number of samples included, P kI Represents joint distribution Take sample (ξ k ,ξ I ), when the sample tends to infinity (or the distribution is a continuous random variable), formula (2-1) can be converted into an integral form:

[0090]

[0091] In formula (2-2), ξ P and ξ k Obedience A random variable, is the joint distribution corresponding to the random variables.

[0092] It should be noted that the uncertainty of the purchase / sale electricity price directly affects the stability of MEMG, making the calculation of the total operating cost of MEMGi, i.e., the calculation of formula (1), full of obstacles. Therefore, the Wasserstein ball model in the distributed robust optimization (DRO) is used to construct the distribution set of purchase / sale electricity prices, and the uncertainty between the purchase / sale electricity prices is expressed by a function.

[0093] Furthermore, the optimization of multiple uncertain agents in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere also includes:

[0094] The uncertainty set of new energy is constructed based on the Wasserstein sphere, and the corresponding expression is:

[0095]

[0096] In formula (8), represents the empirical distribution of renewable energy within MEMGi, R(Ξ) represents the support set of renewable energy, ε i,res is the Wasserstein sphere radius of MEMGi renewable energy.

[0097] It's important to note that renewable energy, like electricity prices, is one of the multiple uncertainties that influence MEMG stability. The output of renewable energy devices installed within a MEMG is highly volatile, and its actual value often differs from the predicted value. Simply substituting the predicted renewable energy output curve into the calculation does not reflect the true system operating costs, so the fluctuation of the predicted value must be taken into account. Traditional robust optimization often uses uncertainty sets such as box-shaped and polyhedron-shaped to define the fluctuation of renewable energy. However, these two types of uncertainty sets can easily lead to overly conservative decisions. To this end, the Wasserstein sphere model from distributional robust optimization (DRO) is used to represent the output of renewable energy through a function.

[0098] Preferably, the objective function of the multi-agent MEMG microgrid alliance is constructed according to the Nash negotiation theory.

[0099] Furthermore, based on the Nash negotiation theory, the objective function of the multi-agent MEMG microgrid alliance is constructed, including:

[0100]

[0101] In formula (9), Represents the maximum benefit of multi-agent MEMG, and the constraints are described Represents the maximum profit of MEMGi when it is run independently, the U i Represents the interests of MEMGi after participating in the cooperation, and minCoM MGi The optimal solutions are opposite to each other, and the constraints This means that the total revenue of any MEMGi after participating in the cooperation is not less than the revenue before the cooperation.

[0102] Preferably, the objective function PR is constructed for the prosumer J within MEMGi based on its own minimization operation cost model i,J .

[0103] For the internal prosumer J of MEMGi, the objective function PR is constructed based on its own minimization operation cost model. i,J ,include:

[0104]

[0105] In formula (10), PR i,J represents the total cost of the J-th prosumer within MEMGi, represents the cost of electricity purchased by prosumer J from MEMGi, represents the load shifting cost of prosumer J participating in demand response, represents the load interruption cost of prosumer J itself participating in demand response, c1 and c2 represent the unit shifting cost and unit load shedding cost respectively.

[0106] Furthermore, the constraints of the prosumer model include:

[0107]

[0108] Formula (3-1) represents the load balancing constraint of producer / consumer J based on demand response, where and They represent the original load, shiftable load variable and interruptible load variable of prosumer J at time t, is the actual load variable of prosumer J after demand response at time t, is the Lagrange multiplier constrained by this equality;

[0109] Furthermore, the constraints of the prosumer model include:

[0110]

[0111] Equations (3-2), (3-3), and (3-4) represent the constraints on the interruptible and shiftable loads of the producer / consumer J at each moment t, where represents the upper limit of the interruptible load within the prosumer J at time t, and It represents the ratio of the maximum transferable load of prosumer J to the original load at time t, and μ i,J represent the multipliers of inequality and equality constraints respectively;

[0112] Furthermore, the constraints of the prosumer model include:

[0113]

[0114] Formula (3-5) represents the lower limit of electricity purchase by prosumer J from MEMGi. is the corresponding multiplier;

[0115] Furthermore, the constraints of the prosumer model include:

[0116]

[0117] Formula (3-6) is the power balance constraint based on DRCC within the prosumer, which is mainly used to deal with the uncertainty of photovoltaic output within the lower-level prosumer J. Its meaning is the photovoltaic random variable Under the worst distribution, the power supply demand within the prosumer can still be met with a probability of at least 1-β, where the corresponding parameter β is the violation probability of DRCC.

[0118] Preferably, the boundary support method is used to reconstruct equation (3-6):

[0119]

[0120] Where, and is an auxiliary variable, N pv is the number of historical photovoltaic samples of the Jth prosumer MEMGi at time t, and They represent the upper and lower limits of the photovoltaic historical data of the prosumer at time t, It represents the historical data of the producer / consumer J at time t, ε pv is a random variable The corresponding Wasserstein sphere radius.

[0121] Further, optimize the reconstruction process:

[0122]

[0123] Where, and is an auxiliary variable, N pv is the number of historical photovoltaic samples of the Jth prosumer MEMGi at time t, and They represent the upper and lower limits of the photovoltaic historical data of the prosumer at time t, It represents the I-th historical data of the producer / consumer J at time t.

[0124] The optimal solution of formula (3-6-5) is a random variable Satisfy the strict lower bound of the distributional robustness constraint. Construct the electric power balance constraint of the lower-level prosumer model:

[0125]

[0126] Where, is the multiplier of the equality constraint.

[0127] Furthermore, the MEMG-prosumer model is solved after equivalent transformation.

[0128] Optionally, based on the duality theory, equations (4) and (5) can be equivalently transformed into:

[0129]

[0130] In formulas (11), (12), and (13), and is the Lagrange multiplier, N pb and N ps are the total number of samples corresponding to the purchase / sale electricity price at time t, and is the purchase / sale electricity price corresponding to the I-th sample at time t.

[0131] Optionally, based on the mean inequality, Equation (9) can be equivalently transformed into:

[0132]

[0133] Based on ADMM combined with CCG algorithm, equations (14) and (15) are solved in a distributed manner.

[0134] This embodiment takes three MEMGs as an example to provide a model iteration process of the alternating direction multiplier method (ADMM) combined with the constraint generation algorithm (CCG):

[0135] Step 1): Set the original residual and the dual residual Initialize the number of iterations k = 0,

[0136] Step 2): and Substitute the two-stage distributed robust optimization (DRO) model of MEMG1 into the model, convert it into a two-stage robust optimization (RO) model through the convex optimization mean method, and use the CCG algorithm to solve it. When the CCG algorithm converges, the obtained and Substitute into the distributed optimization models of MEMG2 and MEMG3 respectively.

[0137] Step 3): and Substitute the two-stage robust optimization model of MEMG2 into the model, convert it into a two-stage robust optimization through the convex optimization mean method, and use the CCG algorithm to solve it. When the CCG algorithm converges, the obtained and Substitute into the distributed optimization models of MEMG1 and MEMG3 respectively.

[0138] Step 4): and Substitute the two-stage robust optimization model of MEMG3 into the model, convert it into a two-stage robust optimization through the convex optimization mean method, and use the CCG algorithm to solve it. When the CCG algorithm converges, the obtained and Substitute into the distributed optimization models of MEMG1 and MEMG2 respectively.

[0139] Step 5): Update the Lagrange multiplier:

[0140]

[0141] Step 6): Calculate the original residual and the dual residual:

[0142]

[0143] Step 7): Determine the convergence conditions:

[0144]

[0145] If the convergence condition is met, the iteration is terminated; otherwise, let k = k + 1 and repeat steps 2-7).

[0146] This embodiment of the present invention first constructs a two-layer optimization framework based on the typical MEMG architecture, with the MEMG at the upper layer and internal prosumers at the lower layer. Secondly, by analyzing sample data on MEMG's own renewable energy, electricity purchase / sale prices, and internal prosumers' photovoltaic performance, and combining Wasserstein fuzzy sets, a two-stage DRO model for the MEMG and a distributed robust chance constraint model for the internal prosumers are constructed, respectively. This improves the robustness of the system's operational decisions. Finally, through the cooperative game of multi-agent MEMGs, the overall operational economic efficiency of the system is further improved.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-agent MEMG scheduling method based on distributed robust optimization, characterized in that: The method comprises: Construct a multi-agent MEMG-prosumer operation model with MEMG at the upper layer and prosumers at the lower layer; For a single MEMGi, the objective function minCoM is constructed based on its own minimization of operating cost. MGi ; The minCoM MGi Represents the minimum value of the total operating cost of MEMGi; Optimizing multiple uncertain agents in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere; According to Nash negotiation theory, the objective function of the multi-agent MEMG microgrid alliance is constructed; For the internal prosumer J of MEMGi, the objective function PR is constructed based on its own minimization operation cost model. i,J , the PR i,J represents the total cost of the J-th prosumer within the MEMGi; Performing equivalent transformation on the MEMG-prosumer operation model and solving it; On the MEMG side, the amount of electricity, natural gas, power consumption of electrical equipment and charge / discharge power of energy storage equipment interacting between the entities are collected through metering equipment. The objective function minCoM MGi The expression is: In formula (1), minCoM MGi represents the minimum value of the total operating cost of MEMGi, Represents the profit of MEMG from selling electricity to prosumers, Represents the profit of P2P interaction between MEMGs, Represents the electricity purchase cost of MEMGi and the upper grid, Represents the profit from electricity sales, represents the cost of the internal gas turbine equipment, Represents the cost of electric boiler equipment, Represents the cost of energy storage equipment, μ g Represents the unit natural gas purchase cost, c m represents the unit operation and maintenance cost of the electric boiler, c E represents the unit deterioration cost of energy storage, represents the natural gas purchase amount of the gas turbine at time t, represents the power consumption of the electric boiler at time t, and Respectively represent the charging / discharging power of energy storage at time t; In formula (2), On behalf of MEMGi, the electricity price set for the Jth prosumer is determined. represents the electricity purchasing strategy decided by prosumer J based on the electricity price set by MEMGi, and m is the number of prosumers within MEMGi; In formula (3), represents the interactive electricity price of MEMGi and MEMGj at time t, and Represents the energy interaction between the two at time t; when If it is greater than 0, it means that MEMGi sells electricity to MEMGj; otherwise, it means that MEMGi buys electricity from MEMGj; Formulas (4) and (5) represent the expected value of the electricity purchase cost / electricity sales profit of MEMGi and the upper power grid under the worst distribution of electricity purchase / sale prices, respectively. and represent the random variables of the purchase / sale electricity price of MEMGi and the upper power grid, and They represent the amount of electricity purchased / sold at time t respectively; The objective function PR is constructed for the internal prosumer J of MEMGi based on its own minimization operation cost model. i,J ,include: In formula (10), PR i,J represents the total cost of the J-th prosumer within MEMGi, represents the cost of electricity purchased by prosumer J from MEMGi, represents the load shifting cost of prosumer J participating in demand response, represents the load interruption cost of prosumer J itself participating in demand response, c1 and c2 represent the unit shifting cost and unit load shedding cost respectively.

2. The multi-agent MEMG scheduling method based on distributed robust optimization according to claim 1 is characterized in that: The Wasserstein sphere-based optimization of multiple uncertain agents in the multi-agent MEMG-prosumer operation model includes: The distribution set of electricity purchase / sale prices is represented by the fuzzy set based on the Wasserstein ball: In formulas (6) and (7), and They represent the empirical distribution of the purchase / sale electricity price at time t, B(Ξ) and S(Ξ) represent the set of probability distributions of the purchase / sale electricity price, and P represents the distribution within the set whose distance from the empirical distribution is less than or equal to ε.

3. The multi-agent MEMG scheduling method based on distributed robust optimization according to claim 2 is characterized in that: The optimization of multiple uncertain agents in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere further includes: The uncertainty set of new energy is constructed based on the Wasserstein sphere, and the corresponding expression is: In formula (8), represents the empirical distribution of renewable energy within MEMGi, R(Ξ) represents the support set of renewable energy, ε i,res is the Wasserstein sphere radius of MEMGi renewable energy.

4. The multi-agent MEMG scheduling method based on distributed robust optimization according to claim 1 is characterized in that: According to the Nash negotiation theory, the objective function of the multi-agent MEMG microgrid alliance is constructed, including: In formula (9), Represents the maximum benefit of multi-agent MEMG, and the constraints are described Represents the maximum profit of MEMGi when it is run independently, the U i Represents the interests of MEMGi after participating in the cooperation, and minCoM MGi The optimal solutions are opposite to each other, and the constraints This means that the total revenue of any MEMGi after participating in the cooperation is not less than the revenue before the cooperation.

5. The multi-agent MEMG scheduling method based on distributed robust optimization according to claim 1 is characterized in that: The MEMG-prosumer operation model is converted into an equivalent solution and then solved, including: Based on the duality theory, equations (4) and (5) are equivalently transformed into: In formulas (11), (12), and (13), and is the Lagrange multiplier, N pb and N ps are the total number of samples corresponding to the purchase / sale electricity price at time t, and is the purchase / sale electricity price corresponding to the I-th sample at time t.

6. The multi-agent MEMG scheduling method based on distributed robust optimization according to claim 5 is characterized in that: The equivalent conversion and solution of the MEMG-prosumer model further includes: based on the mean inequality, equivalently converting equation (9) into: Based on ADMM combined with CCG algorithm, equations (14) and (15) are solved in a distributed manner.

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