Multi-microgrid cooperative scheduling method based on green power equalization and time-sharing response

Abstract

The invention provides a multi-microgrid collaborative scheduling method based on green power equalization and time-sharing response, and relates to the fields of microgrid collaborative optimization, new energy power generation application and demand side response. The method comprises the following steps: S1, establishing various distributed power supply models and demand side response models in the multi-microgrid, wherein the distributed power supply models comprise a photovoltaic equipment model, a wind power equipment model, a micro gas turbine model and an energy storage system model; s2, constructing a three-layer hybrid game model of multiple micro-grids, wherein the three-layer hybrid game model comprises an upper-layer micro-grid dispatching model, a middle-layer micro-grid-user non-cooperative game model and a lower-layer cooperative game model based on a green power equilibrium distribution factor; and S3, solving the three-layer mixed game model by adopting ENGO, and outputting an optimal scheduling scheme of the multi-microgrid. According to the method, the problem of supply and demand mismatching caused by high-proportion distributed new energy grid connection conditions and regional differences among different micro-grids is solved, and the transaction strategy and the scheduling scheme of the multiple micro-grids are optimized.

Description

Technical Field

[0001] The present invention relates to the fields of microgrid collaborative optimization, new energy power generation application, and demand-side response, and in particular to a multi-microgrid collaborative scheduling method based on green power balancing and time-sharing response. Background Art

[0002] With the rapid development of distributed power sources, the power system is undergoing a profound transformation from a traditional centralized system to a distributed, multi-microgrid system. During this transformation, the single transaction model used by traditional power systems and distribution networks is no longer adaptable to the dynamic, multi-party transactions across multiple microgrids. Furthermore, the advancement of distributed renewable energy generation technologies has reduced the matching between sources and loads across microgrids, further exacerbating the complexity of system operation. Against this backdrop, establishing a user demand response mechanism that adapts to the complex multi-microgrid trading market has become a major research challenge.

[0003] Existing technologies primarily employ non-cooperative game theory to solve peer-to-peer (P2P) energy trading problems between microgrids, seeking Nash equilibrium solutions that are superior to microgrid-distribution network trading models. However, this approach has significant limitations: it treats each microgrid as a completely independent entity, ignoring the holistic nature of multiple microgrids as a community of shared interests. This approach cannot effectively address load disparities between regions, nor can it achieve balanced distribution of power resources, ultimately making it difficult to obtain an optimal scheduling solution for multiple microgrids.

[0004] In demand-side response research, existing technologies generally use time-of-use pricing to adjust user electricity usage behavior, and establish the relationship between price adjustments and load adjustments by constructing a price elasticity matrix. However, this approach has inherent flaws: the price elasticity matrix, constructed based on linear equations, is overly simplified and cannot accurately describe user responses to price changes.

[0005] Therefore, there is an urgent need to build a more complete multi-microgrid collaborative model and develop a more accurate demand response description method to break through the technical bottlenecks in existing technologies. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-microgrid coordinated scheduling method based on green electricity balancing and time-sharing response, to solve the supply and demand mismatch problem caused by the grid connection conditions of a high proportion of distributed new energy and the regional differences between different microgrids, to optimize the trading strategies and scheduling schemes of multiple microgrids, to achieve the optimal economic operation of multiple microgrids, to accurately characterize the characteristics of user electricity consumption behavior, and to fully release the flexible adjustment potential of the demand side in the coordinated operation of microgrids.

[0007] To achieve the above objectives, the present invention provides a multi-microgrid coordinated scheduling method based on green power balancing and time-sharing response, comprising the following steps: Step S1: Establish various distributed power supply models and demand-side response models in multiple microgrids. The distributed power supply models include photovoltaic equipment models, wind power equipment models, micro gas turbine models, and energy storage system models. Step S2: construct a three-layer hybrid game model of multiple microgrids, including an upper-layer microgrid dispatch model, a middle-layer microgrid-user non-cooperative game model, and a lower-layer cooperative game model based on green electricity equilibrium allocation factors; Step S3: Use ENGO to solve the three-layer hybrid game model and output the optimal scheduling plan for multiple microgrids.

[0008] Preferably, the parameters and equipment output constraints of the photovoltaic equipment model and the wind power equipment model are as follows: ; ; in, Represents the output power of the photovoltaic device model; Indicates the output power of the wind power equipment model; Indicates the rated output power of the photovoltaic equipment model; Indicates the rated output power of the wind power equipment model; 、 、 、 Respectively represent the current light intensity, rated light intensity, current ambient temperature and rated ambient temperature of the photovoltaic equipment model; Represents the temperature influence coefficient of the photovoltaic device model; 、 、 and They represent the current wind speed, cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine model respectively.

[0009] Preferably, the parameters of the micro gas turbine model and the equipment output constraints are as follows: ; in, 、 They represent the electrical output power of the micro gas turbine model respectively; Indicates the calorific value of natural gas; represents the electrical energy conversion efficiency of the micro gas turbine model; 、 They represent the lower and upper limits of the output of the micro gas turbine model respectively.

[0010] Preferably, the parameters of the energy storage system model and the energy storage charging and discharging constraints are as follows: ; in, The energy storage system model is Energy storage at all times; The energy storage system model is Energy storage at all times; 、 Respectively represent the charging or discharging amount of the energy storage system model; 、 Respectively represent the charging efficiency and discharging efficiency of the energy storage system model; The energy storage system model is Charge level at the moment; The energy storage system model is Discharge amount at the moment; Indicates the upper limit of the charging capacity of the energy storage system model; Indicates the lower limit of the charging capacity of the energy storage system model; Indicates the upper limit of the discharge capacity of the energy storage system model; Indicates the lower limit of the discharge capacity of the energy storage system model; Represents the storage energy of the energy storage system model; Indicates the upper limit of energy storage capacity of the energy storage system model; Indicates the lower limit of the energy storage capacity of the energy storage system model.

[0011] Preferably, the demand-side response model guided by electricity price is based on the time-sharing reference dependence and loss aversion theory, and its electricity price response coefficient is The specific expression is: ; in, express Time electricity price The electricity price response coefficient of the load response quantity at the moment; Indicates time-of-use electricity price Time electricity price The elastic coefficient of the momentary load response; express The difference between the electricity price at that moment and the average electricity price; Indicates the electricity price during the flat period; 、 Represent the minimum electricity price and the maximum electricity price respectively; represents the loss aversion coefficient; Represents the peak time set of electricity prices; Represents the set of electricity price flat period moments; Represents the set of valley time periods for electricity prices; Based on electricity price response coefficient Constructing an electricity price elasticity matrix , and then the load response at each moment is obtained. The specific expression is: ; ; in, express The load response at the time; express Always respond to the previous electricity price; Indicates the load response to changes in unit electricity price; Finally, it is necessary to ensure that the power generation of the microgrid is equal to the load, that is, to ensure the energy balance constraint of the microgrid. The specific expression is as follows: ; in, express The electrical output power of the micro gas turbine model at the moment; The energy storage system model is Discharge amount at the moment; and Respectively Output power of photovoltaic equipment model and wind power equipment model at each moment; The energy storage system model is Charge level at the moment; express The load demand of the microgrid at each moment.

[0012] Preferably, in the upper microgrid dispatch model, the economic objective function of a single microgrid is The specific expression is: ; in, Indicates the The economic cost of a microgrid under this dispatch scheme; represents the natural gas cost within the system; represents the carbon trading cost; and Respectively represent the investment and operation and maintenance costs of equipment in the microgrid; represents the penalty cost of supply-demand imbalance; represents the grid interaction cost.

[0013] Preferably, the specific expression of the mid-level microgrid-user non-cooperative game model is: ; ; ; ; in, represents a set of participants; represents a microgrid; Represents a user; represents the electricity price setting price of the microgrid; Indicates the load response of the user; 、 They represent the power generation cost of the microgrid and the electricity price of the user respectively; represents the economic cost brought about by the electricity price response mechanism; represents the equipment adjustment cost; Indicates the Expression of the mid-level microgrid-user non-cooperative game model of a microgrid.

[0014] Preferably, the specific expression of the lower-layer cooperative game model based on the green electricity equilibrium allocation factor is: ; ; ; in, Represents the modeling of each game subject, including cluster energy operators and microgrids; represents the cluster energy operator's revenue; represents the multi-microgrid benefit; represents the transaction costs between cluster energy operators and multiple microgrids; represents the transaction costs between cluster energy operators and distribution networks; represents the cost of power transmission between microgrids; Indicates the Transaction costs between individual microgrids and cluster energy operators; represents the microgrid transaction price; represents the microgrid transaction volume; It represents the expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level; Indicates the The power generation cost of a microgrid; represents the number of microgrids that make up the multi-microgrid; Indicates the The green efficiency benefit of a microgrid is expressed as follows: ; ; ; ; in, express Moment Green electricity balance allocation factor of each microgrid; and Respectively Output power of photovoltaic equipment model and wind power equipment model at each moment; The energy storage system model is Energy storage at all times; 、 Respectively The weight of the impact of source load and energy storage conditions on green electricity absorption capacity at the moment; express The load response at the time; express Time has come The amount of green electricity purchased by the microgrid at any given moment; The total economic cost of multiple microgrids under this scheduling scheme for: .

[0015] Preferably, in step S3, using ENGO to solve the three-layer hybrid game model specifically includes: Step S301: In the upper-layer microgrid dispatch model, set the parameters of ENGO, including the maximum number of iterations, optimization variable dimension, and population size; Step S302: setting correlation coefficients and output constraints for various distributed power supply models in the microgrid; wherein the correlation coefficients are parameters of various distributed power supply models, and the output constraints include equipment output constraints, energy storage charging and discharging constraints, and energy balance constraints; Step S303: Use ENGO to search for the optimal operation plan for each microgrid and calculate the fitness of each microgrid, that is, the economic objective function of each microgrid. When ENGO reaches the maximum number of iterations, it outputs the optimal scheduling plan with the lowest economic cost for each microgrid, that is, the solution of the upper-level microgrid scheduling model; Step S304: In the middle-level microgrid-user non-cooperative game model, user load information and the solution of the upper-level microgrid scheduling model are input; Step S305: Use ENGO to search for user electricity price schemes and user load response quantities in the mid-level microgrid-user non-cooperative game model; Step S306: Based on the demand-side response model of time-sharing reference dependence and loss aversion, adjust the user load response amount so that the load response amount meets the user's needs and decision-making psychology; at the same time, adjust the upper-level scheduling plan of the microgrid so that the economic cost of the middle-level optimization result is better than the upper-level scheduling result; Step S307: When the ENGO iteration reaches the maximum number of iterations, the optimal solution set that satisfies both the microgrid and the user is output, that is, the scheduling result of the mid-level microgrid-user non-cooperative game model; Step S308: In the lower-level cooperative game model based on the green electricity balanced allocation factor, the dispatch results of the middle-level microgrid-user non-cooperative game model are input to calculate the green electricity balanced allocation factor of each microgrid; Step S309: Use ENGO to search and update the energy transaction price of the cluster energy operator. Use the purchase price of electricity and the energy selling price of each microgrid as optimization variables, continuously update and iterate, and combine with the Cplex business solver to calculate the optimal transaction energy of each microgrid. Step S310: When the ENGO iteration reaches the maximum number of iterations, the optimal solution set that satisfies both the cluster energy operator and the multiple microgrids is output, that is, the scheduling result of the lower-level cooperative game model based on the green electricity balanced allocation factor, and the optimal scheduling plan for the multiple microgrids is completed.

[0016] The preferred ENGO is based on the traditional NGO and introduces the elite northern goshawk population, nonlinear convergence factor and elite individual coordination mechanism, which is improved as follows: Introducing an elite population of northern goshawks into the general population, and forming an experience-driven hierarchical search model by dynamically learning the global optimal position information; A nonlinear convergence factor was designed to make the search behavior of the elite northern goshawk population adaptively adjusted during the iterative process. By introducing an elite individual collaboration mechanism, the traditional single-individual hunting behavior is transformed into a group intelligent collaboration model.

[0017] Therefore, the present invention adopts the above-mentioned multi-microgrid coordinated scheduling method based on green power balancing and time-sharing response, and the beneficial technical effects are as follows: (1) A three-layer hybrid game model was constructed. This model systematically defines various distributed power generation models and demand-side response models, and comprehensively considers output constraints including equipment output constraints, energy storage charging and discharging constraints, and energy balance constraints. By constructing the economic cost objective functions of multiple microgrids, microgrids, and cluster energy operators in a layered and subject-specific manner, a solid model foundation was laid for subsequent scheduling optimization.

[0018] (2) The innovative ENGO algorithm introduces an elite population of northern goshawks based on the traditional NGO algorithm. By dynamically learning the global optimal position information, it forms an experience-driven hierarchical search model. A nonlinear convergence factor is designed to make the search behavior of the elite northern goshawk population adaptively adjust with the iterative process. The elite individual coordination mechanism transforms the traditional single-individual hunting behavior into group intelligent collaboration. These improvements significantly enhance the performance of the algorithm in multi-stakeholder coordination optimization.

[0019] (3) A demand-side response model based on time-of-use reference dependence and loss aversion was established. The prospect theory was innovatively introduced to break through the limitations of the traditional demand response model. By constructing a dynamic electricity price system in the peak-flat-valley period and taking the flat electricity price as the behavioral reference benchmark, a demand-side response model with relative electricity price reference dependence was established. In combination with the loss aversion theory, an asymmetric electricity price adjustment strategy was designed to more accurately characterize the nonlinear perception characteristics of users to the rise and fall of electricity prices.

[0020] (4) A multi-microgrid dispatch model based on green power balancing factors was established. This model quantitatively evaluates the differences in green power consumption capacity of each microgrid and establishes an energy trading mechanism across microgrids. The innovative introduction of green power allocation factors drives the spatiotemporal redistribution of renewable energy generation. Cluster energy operators coordinate and optimize the three types of transaction costs, ensuring supply and demand matching while improving overall economic efficiency, effectively promoting renewable energy consumption and multi-agent collaborative operations.

[0021] Practical application results show that the ENGO of this invention reduces the solution cost by 10.15% compared to traditional NGOs and improves user energy efficiency satisfaction by 0.05. The demand-side response model based on time-sharing reference dependence and loss aversion proposed in this invention effectively alleviates the system's energy supply pressure and significantly improves the degree of supply and demand matching. Based on the green electricity balanced allocation factor, this invention successfully realizes the redistribution of green electricity among multiple microgrids within the distribution network area, reducing the dependence of multiple microgrids on the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to the present invention; Figure 2 A comparison chart of the total economic costs of multiple microgrids during the optimization iteration process of NGOs and ENGOs; Figure 3 The comparison chart of the total economic cost of the optimal multi-microgrid between NGO and ENGO is shown. DETAILED DESCRIPTION

[0023] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0024] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0025] Example 1

[0026] like Figure 1 As shown, a multi-microgrid coordinated scheduling method based on green power balancing and time-sharing response includes the following steps: Step S1: Establish various distributed power supply models and demand-side response models in multiple microgrids. The distributed power supply models include photovoltaic equipment models, wind power equipment models, micro gas turbine models, and energy storage system models.

[0027] The power generation of photovoltaic and wind turbines is determined by weather conditions. The parameters and output constraints of the photovoltaic and wind turbine models are as follows: ; ; in, Represents the output power of the photovoltaic device model; Indicates the output power of the wind power equipment model; Indicates the rated output power of the photovoltaic equipment model; Indicates the rated output power of the wind power equipment model; 、 、 、 Respectively represent the current light intensity, rated light intensity, current ambient temperature and rated ambient temperature of the photovoltaic equipment model; Represents the temperature influence coefficient of the photovoltaic device model; 、 、 and They represent the current wind speed, cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine model respectively.

[0028] The micro gas turbine is an important power generation device in a microgrid. The parameters and output constraints of the micro gas turbine model are as follows: ; in, 、 They represent the electrical output power of the micro gas turbine model respectively; Indicates the calorific value of natural gas, ; represents the electrical energy conversion efficiency of the micro gas turbine model; 、 They represent the lower and upper limits of the output of the micro gas turbine model respectively.

[0029] The energy storage system is a device that achieves a balanced distribution of green electricity over time, enabling time shifting of energy. The parameters of the energy storage system model and the energy storage charging and discharging constraints are as follows: ; in, The energy storage system model is Energy storage at all times; The energy storage system model is Energy storage at all times; 、 Respectively represent the charging or discharging amount of the energy storage system model; 、 Respectively represent the charging efficiency and discharging efficiency of the energy storage system model; The energy storage system model is Charge level at the moment; The energy storage system model is Discharge amount at the moment; Indicates the upper limit of the charging capacity of the energy storage system model; Indicates the lower limit of the charging capacity of the energy storage system model; Indicates the upper limit of the discharge capacity of the energy storage system model; Indicates the lower limit of the discharge capacity of the energy storage system model; Represents the storage energy of the energy storage system model; Indicates the upper limit of energy storage capacity of the energy storage system model; Indicates the lower limit of the energy storage capacity of the energy storage system model.

[0030] User load typically consists of a base load and flexible load. Flexible load represents less urgent user demand and can be guided and adjusted to a certain extent through time-of-use pricing strategies. To more accurately describe user responses to electricity consumption, this paper improves the traditional price elasticity matrix based on prospect theory, a key conclusion in economics. Its core principles are reference dependence and loss aversion. Reference dependence refers to the fact that user demand adjustment decisions are based on a relative reference price, rather than an absolute value. Based on this, this paper introduces three price periods: peak, flat, and valley. The flat period serves as a reference price for user electricity prices and lasts the longest compared to the peak and valley periods. During the peak and valley periods, the flat price is increased or decreased to varying degrees based on the source and load conditions at different times. Loss aversion refers to the perception that a user experiences a loss that is 2-2.5 times more painful than a gain for the same amount. Therefore, price adjustments during peak periods are typically larger than those during valley periods.

[0031] The demand-side response model guided by electricity prices is based on the time-sharing reference dependence and loss aversion theory, and its electricity price response coefficient The specific expression is: ; in, express Time electricity price The electricity price response coefficient of the load response quantity at the moment; Indicates time-of-use electricity price Time electricity price The elastic coefficient of the momentary load response; express The difference between the electricity price at that moment and the average electricity price; Indicates the electricity price during the flat period; 、 Represent the minimum electricity price and the maximum electricity price respectively; represents the loss aversion coefficient, ; Represents the peak time set of electricity prices; Represents the set of electricity price flat period moments; Represents the set of valley time periods for electricity prices.

[0032] Based on electricity price response coefficient Constructing an electricity price elasticity matrix , and then the load response at each moment is obtained. The specific expression is: ; ; in, express The load response at the time; express Always respond to the previous electricity price; Indicates the load response to changes in unit electricity price.

[0033] Finally, it is necessary to ensure that the power generation of the microgrid is equal to the load, that is, to ensure the energy balance constraint of the microgrid. The specific expression is as follows: ; in, express The electrical output power of the micro gas turbine model at the moment; The energy storage system model is Discharge amount at the moment; and Respectively Output power of photovoltaic equipment model and wind power equipment model at each moment; The energy storage system model is Charge level at the moment; express The load demand of the microgrid at each moment.

[0034] Step S2: construct a three-layer hybrid game model of multiple microgrids.

[0035] For A multi-microgrid composed of microgrids is constructed, and its upper-level microgrid scheduling model, middle-level microgrid-user non-cooperative game model, and lower-level cooperative game model based on green electricity equilibrium allocation factors are constructed respectively.

[0036] In the upper-level microgrid dispatch model, the economic cost of each microgrid is minimized by independently adjusting the dispatching scheme of each microgrid within the distribution network area. The economic objective function of a single microgrid is: The specific expression is: ; in, Indicates the The economic cost of a microgrid under this dispatch scheme; represents the natural gas cost within the system; represents the carbon trading cost; and Respectively represent the investment and operation and maintenance costs of equipment in the microgrid; represents the penalty cost of supply-demand imbalance; represents the grid interaction cost.

[0037] Each mid-level microgrid-user non-cooperative game model consists of two game entities: the microgrid and the user. The microgrid uses the user's electricity price and the response incentive compensation price as the game strategy, and the user responds accordingly based on the electricity price. The specific expression of the mid-level microgrid-user non-cooperative game model is: ; ; ; ; in, represents a set of participants; represents a microgrid; Represents a user; represents the electricity price setting price of the microgrid; Indicates the load response of the user; 、 They represent the power generation cost of the microgrid and the electricity price of the user respectively; represents the economic cost brought about by the electricity price response mechanism; represents the equipment adjustment cost; Indicates the Expression of the mid-level microgrid-user non-cooperative game model of a microgrid.

[0038] Because microgrids differ in their distributed energy structure, load demands, and climate environments, they can trade energy among themselves to balance supply and demand and maximize profits. The green electricity balance allocation factor assesses the green electricity consumption level of each microgrid and redistributes renewable energy generation across multiple microgrids via inter-microgrid transmission lines to match the environmental and load differences among these microgrids. Transactions between microgrids are determined by cluster energy operators, whose optimization goal is to maximize their own profits. When managing energy, they primarily consider three aspects of transaction costs: transactions between microgrids, transactions between microgrids and the distribution network, and operating and maintenance costs during transactions.

[0039] The specific expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level is: ; ; ; in, Represents the modeling of each game subject, including cluster energy operators and microgrids; represents the cluster energy operator's revenue; represents the multi-microgrid benefit; represents the transaction costs between cluster energy operators and multiple microgrids; represents the transaction costs between cluster energy operators and distribution networks; represents the cost of power transmission between microgrids; Indicates the Transaction costs between individual microgrids and cluster energy operators; represents the microgrid transaction price; represents the microgrid transaction volume; It represents the expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level; Indicates the The power generation cost of a microgrid; represents the number of microgrids that make up the multi-microgrid; Indicates the The green efficiency benefit of a microgrid is a negative number, which can motivate the microgrid to sell or purchase green electricity. The specific expression is: ; ; ; ; in, express Moment The green electricity balanced distribution factor of a microgrid. The larger the value, the more green electricity the microgrid can accommodate. and Respectively Output power of photovoltaic equipment model and wind power equipment model at each moment; The energy storage system model is Energy storage at all times; 、 Respectively The weight of the impact of source load and energy storage conditions on green electricity absorption capacity at the moment; express The load response at the time; express Time has come The amount of green electricity purchased by the microgrid at any given moment.

[0040] The total economic cost of multiple microgrids under this scheduling scheme for: .

[0041] Step S3: Use the Elite-Guided Northern Goshawk (ENGO) algorithm to solve the three-layer hybrid game model and output the optimal dispatching plan for multiple microgrids. Specifically, it includes: Step S301: In the upper-layer microgrid dispatch model, set the parameters of ENGO, including the maximum number of iterations, optimization variable dimension, and population size.

[0042] Among them, the population position is the dimension of the optimization variable. In the upper-level microgrid scheduling model, the optimization variables include the unit output plan of the microgrid at each moment.

[0043] Step S302: Set the correlation coefficients and output constraints of various distributed power models in the microgrid. The correlation coefficients are the parameters of the various distributed power models described in step S1, such as 、 The output constraints include equipment output constraints, energy storage charging and discharging constraints, and energy balance constraints, and their formulas are described in detail in step S1.

[0044] Step S303: Use ENGO to search for the optimal operation plan for each microgrid.

[0045] Initialize the population positions. Calculate the fitness of each microgrid, i.e., the economic objective function of each microgrid. Iterate and update the population positions. When ENGO reaches the maximum number of iterations, it outputs the optimal dispatch solution for each microgrid at the lowest economic cost, i.e., the solution of the upper-level microgrid dispatch model.

[0046] Step S304: In the middle-level microgrid-user non-cooperative game model, user load information and the solution result of the upper-level microgrid scheduling model are input.

[0047] Step S305: Use ENGO to search for user electricity price schemes and user load response quantities in the mid-level microgrid-user non-cooperative game model.

[0048] Step S306: Based on the demand-side response model of time-sharing reference dependence and loss aversion, adjust the user load response to ensure that the adjustment meets the user's needs and decision-making psychology. Simultaneously, adjust the upper-level dispatch plan of the microgrid to ensure that the economic cost of the middle-level optimization result is better than that of the upper-level dispatch result.

[0049] Step S307: When the ENGO iteration reaches the maximum number of iterations, the optimal solution set that satisfies both the microgrid and the user is output, that is, the scheduling result of the mid-level microgrid-user non-cooperative game model.

[0050] Step S308: In the lower-layer cooperative game model based on the green electricity balanced allocation factor, the dispatch result of the middle-layer microgrid-user non-cooperative game model is input to calculate the green electricity balanced allocation factor of each microgrid.

[0051] Step S309: Use ENGO to search and update the energy transaction price of the cluster energy operator, use the purchase price of electricity and the energy selling price of each microgrid as optimization variables, continuously update and iterate, and combine with the Cplex business solver to calculate the optimal transaction energy of each microgrid.

[0052] Step S310: When the ENGO iteration reaches the maximum number of iterations, the optimal solution set that satisfies both the cluster energy operator and the multiple microgrids is output, that is, the scheduling result of the lower-level cooperative game model based on the green electricity balanced allocation factor, and the optimal scheduling plan for the multiple microgrids is completed.

[0053] Among them, ENGO introduced elite northern goshawk populations, nonlinear convergence factors and elite individual coordination mechanisms based on the traditional northern goshawk optimizer (NGO), and made improvements.

[0054] NGO is a metaheuristic algorithm that simulates the behavior of northern goshawks in catching prey, which is mainly divided into two stages: exploration and exploitation.

[0055] First, the NGO initializes the individual positions of the northern goshawk, which is the set of optimization variables. The specific expression is: ; in, Indicates the first iteration of the population individual locations of northern goshawks; and Represent the maximum and minimum vectors of population positions, respectively, and their dimensions are consistent with the number of optimization variables; Represents a uniform random number, and the range represents .

[0056] The first stage of the northern goshawk's position update is identification and attack. The NGO will randomly select prey to attack. This process will globally explore the variable space to locate the optimal variable location. The update formula is: ; in, Indicates the The location of the prey; Indicates that the interval is Random number; Indicates the The first population in the iteration individual locations of northern goshawks; Represents a random integer between 1 or 2; Indicates the The population of the first iteration individual locations of northern goshawks; Indicates the The first population in the iteration The fitness function value corresponding to the individual position of the northern goshawk.

[0057] The second stage of the northern goshawk's position update is pursuit and escape. In this process, the position is updated by simulating the prey's escape and the northern goshawk's pursuit, and then a local search is performed on the local location of the prey.

[0058] However, when solving the three-layer hybrid game model proposed in this invention, NGOs have difficulty in searching for the optimal transaction and scheduling solutions for multiple microgrids due to their relatively simple population search strategy. Therefore, this invention proposes ENGOs.

[0059] An elite population of northern goshawks is introduced into the general population. By dynamically learning the global optimal position information, an experience-driven hierarchical search model is formed. The elite population of northern goshawks has a stronger ability to search and capture prey than ordinary northern goshawks. The update formula of the elite population of northern goshawks in the search phase is: ; in, represents the optimal individual population position.

[0060] The elite population of northern goshawks can learn from the optimal population individuals to hunt, and as the number of iterations increases, the elite population of northern goshawks becomes more and more proficient in hunting. This paper designs a nonlinear convergence factor to simulate this process, allowing the search behavior of the elite northern goshawk population to be adaptively adjusted as the iteration progresses. This strategy can improve the algorithm's early development and later search capabilities. The specific expression is: ; ; in, represents the nonlinear convergence factor; represents the number of population iterations; Indicates the maximum number of iterations of the population.

[0061] In the second stage, an elite individual collaboration mechanism is introduced to transform the traditional hunting behavior of a single individual into a group intelligent collaboration model. The elite northern goshawk population cooperates with other individuals to enhance hunting efficiency. The search formula is: ; in, Indicates the The first population in the iteration Location of individual northern goshawks.

[0062] To verify the effectiveness of the present invention, three microgrids in different regions were selected to form a multi-microgrid. The traditional NGO and the ENGO of the present invention were used to optimize the multi-microgrid, and the total economic cost of the multi-microgrid under the two different algorithms was compared. The experimental results are as follows. Figure 2 and Figure 3 shown.

[0063] Experimental results show that the ENGO of the present invention has significant advantages over the traditional NGO: in terms of optimization effect, ENGO reduces the cost of solution results by 10.15%; in addition, in terms of user experience, user satisfaction with energy efficiency is improved by 0.05.

[0064] Therefore, the present invention adopts the above-mentioned multi-microgrid collaborative scheduling method based on green electricity balancing and time-sharing response, which solves the supply and demand mismatch problem caused by the high proportion of distributed new energy grid connection conditions and the regional differences between different microgrids, optimizes the trading strategy and scheduling scheme of multiple microgrids, realizes the optimal economic operation of multiple microgrids, accurately characterizes the characteristics of user electricity consumption behavior, and provides an innovative technical solution for the optimized scheduling of multiple microgrids.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response, characterized in that: The following steps are involved: Step S1: Establish various distributed power supply models and demand-side response models in multiple microgrids. The distributed power supply models include photovoltaic equipment models, wind power equipment models, micro gas turbine models, and energy storage system models. Step S2: construct a three-layer hybrid game model of multiple microgrids, including an upper-layer microgrid dispatch model, a middle-layer microgrid-user non-cooperative game model, and a lower-layer cooperative game model based on green electricity equilibrium allocation factors; Step S3: Use ENGO to solve the three-layer hybrid game model and output the optimal scheduling plan for multiple microgrids.

2. A multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 1, characterized in that: The parameters and equipment output constraints of the photovoltaic equipment model and wind power equipment model are as follows: ； ； in, Indicates the output power of the photovoltaic device model; Indicates the output power of the wind power equipment model; Indicates the rated output power of the photovoltaic equipment model; Indicates the rated output power of the wind power equipment model; 、 、 、 Respectively represent the current light intensity, rated light intensity, current ambient temperature and rated ambient temperature of the photovoltaic equipment model; Represents the temperature influence coefficient of the photovoltaic device model; 、 、 and They represent the current wind speed, cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine model respectively.

3. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 2 is characterized in that: The parameters and equipment output constraints of the micro gas turbine model are as follows: ； in, 、 They represent the electrical output power of the micro gas turbine model respectively; Indicates the calorific value of natural gas; represents the electrical energy conversion efficiency of the micro gas turbine model; 、 They represent the lower and upper limits of the output of the micro gas turbine model respectively.

4. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 3 is characterized in that: The parameters of the energy storage system model and the energy storage charging and discharging constraints are as follows: ； in, The energy storage system model is Energy storage at all times; The energy storage system model is Energy storage at all times; 、 Respectively represent the charging or discharging amount of the energy storage system model; 、 Respectively represent the charging efficiency and discharging efficiency of the energy storage system model; The energy storage system model is Charge level at the moment; The energy storage system model is Discharge amount at the moment; Indicates the upper limit of the charging capacity of the energy storage system model; Indicates the lower limit of the charging capacity of the energy storage system model; Indicates the upper limit of the discharge capacity of the energy storage system model; Indicates the lower limit of the discharge capacity of the energy storage system model; Represents the storage energy of the energy storage system model; Indicates the upper limit of energy storage capacity of the energy storage system model; Indicates the lower limit of the energy storage capacity of the energy storage system model.

5. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 4 is characterized in that: The demand-side response model guided by electricity prices is based on the time-sharing reference dependence and loss aversion theory, and its electricity price response coefficient The specific expression is: ； in, express Time electricity price The electricity price response coefficient of the load response quantity at the moment; Indicates time-of-use electricity price Time electricity price The elastic coefficient of the momentary load response; express The difference between the electricity price at that moment and the average electricity price; Indicates the electricity price during the flat period; 、 Represent the minimum electricity price and the maximum electricity price respectively; represents the loss aversion coefficient; Represents the peak time set of electricity prices; Represents the set of electricity price flat period moments; Represents the set of valley time periods for electricity prices; Based on electricity price response coefficient Constructing an electricity price elasticity matrix , and then the load response at each moment is obtained. The specific expression is: ； ； in, express The load response at the time; express Always respond to the previous electricity price; Indicates the load response to changes in unit electricity price; Finally, it is necessary to ensure that the power generation of the microgrid is equal to the load, that is, to ensure the energy balance constraint of the microgrid. The specific expression is as follows: ； in, express The electrical output power of the micro gas turbine model at the moment; The energy storage system model is Discharge amount at the moment; and Respectively Output power of photovoltaic equipment model and wind power equipment model at each moment; The energy storage system model is Charge level at the moment; express The load demand of the microgrid at each moment.

6. A multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 5, characterized in that: In the upper-level microgrid dispatch model, the economic objective function of a single microgrid is The specific expression is: ； in, Indicates the The economic cost of a microgrid under this dispatch scheme; represents the natural gas cost within the system; represents the carbon trading cost; and Respectively represent the investment and operation and maintenance costs of equipment in the microgrid; represents the penalty cost of supply-demand imbalance; represents the grid interaction cost.

7. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 6 is characterized in that: The specific expression of the mid-level microgrid-user non-cooperative game model is: ； ； ； ； in, represents a set of participants; represents a microgrid; Represents a user; represents the electricity price setting price of the microgrid; Indicates the load response of the user; 、 They represent the power generation cost of the microgrid and the electricity price of the user respectively; represents the economic cost brought about by the electricity price response mechanism; represents the equipment adjustment cost; Indicates the Expression of the mid-level microgrid-user non-cooperative game model of a microgrid.

8. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 7 is characterized in that: The specific expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level is: ； ； ； in, Represents the modeling of each game subject, including cluster energy operators and microgrids; represents the cluster energy operator's revenue; represents the multi-microgrid benefit; represents the transaction costs between cluster energy operators and multiple microgrids; represents the transaction costs between cluster energy operators and distribution networks; represents the cost of power transmission between microgrids; Indicates the Transaction costs between individual microgrids and cluster energy operators; represents the microgrid transaction price; represents the microgrid transaction volume; It represents the expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level; Indicates the The power generation cost of a microgrid; represents the number of microgrids that make up the multi-microgrid; Indicates the The green efficiency benefit of a microgrid is expressed as follows: ； ； ； ； in, express Moment Green electricity balance allocation factor of each microgrid; and Respectively Output power of photovoltaic equipment model and wind power equipment model at each moment; The energy storage system model is Energy storage at all times; 、 Respectively The weight of the impact of source load and energy storage conditions on green electricity absorption capacity at the moment; express The load response at the time; express Time has come The amount of green electricity purchased by the microgrid at any given moment; The total economic cost of multiple microgrids under this scheduling scheme for: 。 9. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 1 is characterized in that: In step S3, using ENGO to solve the three-layer hybrid game model specifically includes: Step S301: In the upper-layer microgrid dispatch model, set the parameters of ENGO, including the maximum number of iterations, optimization variable dimension, and population size; Step S302: setting correlation coefficients and output constraints for various distributed power models in the microgrid; wherein the correlation coefficients are parameters of various distributed power models, and the output constraints include equipment output constraints, energy storage charging and discharging constraints, and energy balance constraints; Step S303: Use ENGO to search for the optimal operation plan of each microgrid and calculate the fitness of each microgrid, that is, the economic objective function of each microgrid. When ENGO iterates to the maximum number of times, it outputs the optimal scheduling plan with the lowest economic cost for each microgrid, that is, the solution of the upper-level microgrid scheduling model; Step S304: In the middle-level microgrid-user non-cooperative game model, user load information and the solution of the upper-level microgrid scheduling model are input; Step S305: Use ENGO to search for user electricity price schemes and user load response quantities in the mid-level microgrid-user non-cooperative game model; Step S306: Based on the demand-side response model of time-sharing reference dependence and loss aversion, adjust the user load response amount so that the load response amount meets the user's needs and decision-making psychology; at the same time, adjust the upper-level scheduling plan of the microgrid so that the economic cost of the middle-level optimization result is better than the upper-level scheduling result; Step S307: When the ENGO iteration reaches the maximum number of iterations, the optimal solution set that satisfies both the microgrid and the user is output, that is, the scheduling result of the mid-level microgrid-user non-cooperative game model; Step S308: In the lower-level cooperative game model based on the green electricity balanced allocation factor, the dispatch results of the middle-level microgrid-user non-cooperative game model are input to calculate the green electricity balanced allocation factor of each microgrid; Step S309: Use ENGO to search and update the energy transaction price of the cluster energy operator. Use the purchase price of electricity and the energy selling price of each microgrid as optimization variables, continuously update and iterate, and combine with the Cplex business solver to calculate the optimal transaction energy of each microgrid. Step S310: When the ENGO iteration reaches the maximum number of iterations, the optimal solution set that satisfies both the cluster energy operator and the multiple microgrids is output, that is, the scheduling result of the lower-level cooperative game model based on the green electricity balanced allocation factor, and the optimal scheduling plan for the multiple microgrids is completed.

10. The multi-microgrid coordinated dispatching method based on green power balancing and time-sharing response according to claim 1 is characterized in that: Based on traditional NGOs, ENGOs introduced elite northern goshawk populations, nonlinear convergence factors, and elite individual coordination mechanisms, which are specifically manifested as follows: Introducing an elite population of northern goshawks into the general population, and forming an experience-driven hierarchical search model by dynamically learning the global optimal position information; A nonlinear convergence factor was designed to make the search behavior of the elite northern goshawk population adaptively adjusted during the iterative process. By introducing an elite individual collaboration mechanism, the traditional single-individual hunting behavior is transformed into a group intelligent collaboration model.

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