A Multi-Microgrid Cooperative Scheduling Method Based on Green Energy Balance and Time-Sharing Response

Abstract

This invention provides a multi-microgrid collaborative scheduling method based on green energy balance and time-of-use response, relating to microgrid collaborative optimization, renewable energy generation applications, and demand-side response. The method includes: Step S1, establishing various distributed power source models and demand-side response models in the multi-microgrid, including photovoltaic equipment models, wind power equipment models, micro gas turbine models, and energy storage system models; Step S2, constructing a three-layer hybrid game model for the multi-microgrid, including an upper-layer microgrid scheduling model, a middle-layer microgrid-user non-cooperative game model, and a lower-layer cooperative game model based on green energy balance allocation factors; Step S3, using ENGO to solve the three-layer hybrid game model and outputting the optimal scheduling scheme for the multi-microgrid. This invention solves the supply-demand mismatch problem caused by high-proportion distributed renewable energy grid connection conditions and regional differences between different microgrids, optimizing the trading strategy and scheduling scheme of multi-microgrids.

Description

Technical Field

[0001] This invention relates to the fields of microgrid collaborative optimization, new energy power generation applications, and demand-side response, and in particular to a multi-microgrid collaborative scheduling method based on green energy balance and time-sharing response. Background Technology

[0002] With the rapid development of distributed power generation, the power system is undergoing a profound transformation from traditional centralized systems to distributed multi-microgrid systems. During this transformation, the single trading method used in traditional power systems and distribution networks is no longer suitable for the dynamic multi-party trading format of multi-microgrids. At the same time, the development of distributed renewable energy generation technologies has reduced the source-load matching degree among microgrids, further exacerbating the complexity of system operation. Against this backdrop, establishing a user demand response mechanism that adapts to the complex trading market of multi-microgrids has become a significant research challenge.

[0003] Existing technologies primarily employ non-cooperative game theory to address point-to-point (P2P) energy trading between microgrids, seeking Nash equilibrium solutions superior to microgrid-distribution network trading models. However, this approach has significant limitations: it treats each microgrid as a completely independent entity, neglecting the overall characteristics of multiple microgrids as a community of shared interests. This approach fails to effectively address load disparities between different regions and struggles to achieve balanced allocation of power resources, ultimately making it difficult to obtain an optimal overall dispatching scheme for the multiple microgrids.

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

[0005] Therefore, there is an urgent need to build a more complete multi-microgrid collaborative cooperation model and develop more accurate demand response description methods to overcome the technical bottlenecks existing in the current technology. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-microgrid collaborative scheduling method based on green electricity balance and time-sharing response, which solves the supply and demand mismatch caused by high proportion of distributed renewable energy grid connection conditions and regional differences between different microgrids, optimizes the trading strategy and scheduling scheme of multi-microgrids, realizes the optimal economic operation of multi-microgrids, accurately characterizes the electricity consumption behavior of users, and fully releases the flexible adjustment potential of the demand side in the collaborative operation of microgrids.

[0007] To achieve the above objectives, this invention provides a multi-microgrid collaborative scheduling method based on green power balancing and time-sharing response, comprising the following steps:

[0008] Step S1: Establish various distributed power source models and demand-side response models in multi-microgrids. Distributed power source models include photovoltaic equipment models, wind power equipment models, micro gas turbine models, and energy storage system models.

[0009] Step S2: Construct a three-layer hybrid game model for multiple microgrids, including an upper-layer microgrid scheduling model, a middle-layer microgrid-user non-cooperative game model, and a lower-layer cooperative game model based on green electricity equilibrium allocation factors.

[0010] Step S3: Use ENGO to solve the three-layer hybrid game model and output the optimal scheduling scheme for multiple microgrids.

[0011] Preferably, the parameters and power output constraints of the photovoltaic equipment model and the wind power equipment model are as follows:

[0012] ;

[0013] ;

[0014] in, This indicates the output power of the photovoltaic equipment model; This indicates the output power of the wind turbine model; This indicates the rated output power of the photovoltaic equipment model; This indicates the rated output power of the wind turbine model; , , , These represent the current light intensity, rated light intensity, current ambient temperature, and rated ambient temperature of the photovoltaic equipment model, respectively. This represents the temperature influence coefficient of the photovoltaic equipment model; , , and These represent the current wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the wind power equipment model, respectively.

[0015] Preferably, the parameters and power output constraints of the micro gas turbine model are as follows:

[0016] ;

[0017] in, , These represent the electrical output power of the micro gas turbine model; Indicates the calorific value of natural gas; This represents the energy conversion efficiency of a micro gas turbine model; , These represent the lower and upper limits of the output of the micro gas turbine model, respectively.

[0018] Preferably, the parameters of the energy storage system model and the energy storage charge / discharge constraints are as follows:

[0019] ;

[0020] in, Indicates the energy storage system model in Energy storage at all times; Indicates the energy storage system model in Energy storage at all times; , These represent the charging or discharging amount of the energy storage system model, respectively. , These represent the charging efficiency and discharging efficiency of the energy storage system model, respectively. Indicates the energy storage system model in The amount of charge at any given moment; Indicates the energy storage system model in The amount of discharge at any given moment; This indicates the upper limit of the charging capacity of the energy storage system model; This indicates the lower limit of the charging capacity of the energy storage system model; This indicates the upper limit of the discharge capacity of the energy storage system model; This indicates the lower limit of the discharge capacity of the energy storage system model; This represents the energy stored in the energy storage system model; This indicates the upper limit of the energy storage capacity of the energy storage system model; This represents the lower limit of the energy storage capacity of the energy storage system model.

[0021] Preferably, the demand-side response model guided by electricity prices, based on time-of-use reference dependence and loss aversion theory, has an electricity price response coefficient. The specific expression is:

[0022] ;

[0023] in, express Time-of-use electricity price Electricity price response coefficient of load response at any given time; Indicates time-of-use electricity pricing Time-of-use electricity price The elasticity coefficient of the load response at any given time; express The difference between the current electricity price and the electricity price during the off-peak period; This indicates the electricity price during the off-peak period. , These represent the minimum and maximum electricity prices, respectively. Indicates the loss aversion coefficient; This represents the set of peak electricity price periods; Represents the set of times during which electricity prices are at rest; This represents the set of times during off-peak electricity prices;

[0024] Based on electricity price response coefficient Constructing an electricity price elasticity matrix This allows us to obtain the load response at each time point, specifically expressed as:

[0025] ;

[0026] ;

[0027] in, express The load response at any given time; express Responding to current electricity prices; This represents the load response to a unit change in electricity price.

[0028] Finally, it is necessary to ensure that the power generation of the microgrid equals the load, that is, to ensure the energy balance constraint of the microgrid. The specific expression is as follows:

[0029] ;

[0030] in, express The electrical output power of the micro gas turbine model at any given time; Indicates the energy storage system model in The amount of discharge at any given moment; and They represent The output power of the photovoltaic equipment model and the wind power equipment model at any given time; Indicates the energy storage system model in The amount of charge at any given moment; express The load demand of the microgrid at all times.

[0031] Preferably, in the upper-level microgrid dispatch model, the economic objective function of a single microgrid is... The specific expression is:

[0032] ;

[0033] in, Indicates the first The economic cost of a microgrid under this dispatch scheme; Indicates the cost of natural gas within the system; Indicates carbon trading costs; and These represent the investment and operation and maintenance costs of equipment in a microgrid, respectively. This represents the penalty cost for an imbalance between supply and demand; This represents the cost of grid interaction.

[0034] The preferred expression for the non-cooperative game model between the mid-level microgrid and users is as follows:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] in, Represents the set of participants; Indicates a microgrid; Indicates user; This indicates the electricity price set for the microgrid; This indicates the user's load response volume; , These represent the generation cost of the microgrid and the electricity price for users, respectively. This indicates the economic costs associated with the electricity price response mechanism; Indicates equipment adjustment costs; Indicates the first Expression of the mid-level microgrid-user non-cooperative game model for a microgrid.

[0040] Preferably, the specific expression of the lower-level cooperative game model based on the green electricity equilibrium allocation factor is as follows:

[0041] ;

[0042] ;

[0043] ;

[0044] in, This represents the modeling of various game players, including cluster energy operators and microgrids; This indicates the revenue of the cluster energy operator; Indicates the benefits of multiple microgrids; This represents the transaction costs between the cluster energy operator and multiple microgrids; This represents the transaction costs between the cluster energy operator and the distribution network; This indicates the cost of transmitting electricity between microgrids; Indicates the first Transaction costs between microgrids and cluster energy operators; Indicates the microgrid transaction price; Indicates microgrid transaction volume; This represents the expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level; Indicates the first The cost of generating electricity in a microgrid; Indicates the number of microgrids that make up the multi-microgrid system;

[0045] Indicates the first The green efficiency benefits of a microgrid are specifically expressed as follows:

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] in, express Time of the first Green electricity balance allocation factor for microgrids; and They represent The output power of the photovoltaic equipment model and the wind power equipment model at any given time; Indicates the energy storage system model in Energy storage at all times; , They represent The weighting of the impact of current source load and energy storage on green electricity absorption capacity; express The load response at any given time; express Time's up The amount of green electricity purchased by the microgrid at any time;

[0051] Total economic cost of multiple microgrids under this dispatch scheme for:

[0052] .

[0053] Preferably, in step S3, solving the three-layer hybrid game model using ENGO specifically includes:

[0054] Step S301: In the upper-level microgrid scheduling model, set the parameters of ENGO, including the maximum number of iterations, the dimension of optimization variables, and the population size;

[0055] Step S302: Set the correlation coefficients and output constraints for various distributed power source models in the microgrid; wherein, the correlation coefficients are the parameters of various distributed power source models, and the output constraints include equipment output constraints, energy storage charging and discharging constraints, and energy balance constraints.

[0056] Step S303: Use ENGO to search for the optimal operating scheme of each microgrid and calculate the fitness of each microgrid, i.e., the economic objective function of each microgrid. After ENGO iterates to the maximum number of iterations, output the optimal scheduling scheme under the lowest economic cost for each microgrid, i.e., the solution result of the upper-level microgrid scheduling model;

[0057] Step S304: In the mid-level microgrid-user non-cooperative game model, input the user load information and the solution results of the upper-level microgrid scheduling model;

[0058] Step S305: Use ENGO to search for user electricity pricing schemes and user load response in the mid-level microgrid-user non-cooperative game model respectively;

[0059] Step S306: Based on the time-sharing reference dependence and loss aversion demand-side response model, adjust the user load response to make the load response conform to the user's needs and decision-making psychology; at the same time, adjust the upper-level dispatch scheme of the microgrid so that the economic cost of the middle-level optimization result is better than that of the upper-level dispatch result.

[0060] Step S307: When the ENGO iteration reaches the maximum number of iterations, output the optimal solution set that satisfies both the microgrid and the users, i.e., the scheduling result of the mid-level microgrid-user non-cooperative game model.

[0061] Step S308: In the lower-level cooperative game model based on the green electricity balance allocation factor, input the scheduling results of the middle-level microgrid-user non-cooperative game model, and calculate the green electricity balance allocation factor of each microgrid.

[0062] Step S309: Use ENGO search to update the energy trading price of the cluster energy operator, use the purchase price of electricity and the selling price of energy of each microgrid as optimization variables, continuously update and iterate, and combine with the Cplex commercial solver to calculate the optimal trading energy of each microgrid.

[0063] Step S310: When the ENGO iteration reaches the maximum number of iterations, output the optimal solution set that satisfies both the cluster energy operator and the multiple microgrids, that is, the scheduling result of the lower-level cooperative game model based on the green electricity balance allocation factor, and complete the optimal scheduling scheme of the multiple microgrids.

[0064] The preferred approach is to improve upon traditional NGOs by introducing an elite northern goshawk population, a nonlinear convergence factor, and an elite individual collaboration mechanism. Specifically, this is manifested in the following ways:

[0065] An elite population of Northern Goshawks was introduced into the general population, and an experience-driven hierarchical search model was formed by dynamically learning the global optimal position information.

[0066] A nonlinear convergence factor was designed to enable the search behavior of the elite Northern Goshawk population to adaptively adjust with the iteration process.

[0067] By introducing an elite individual collaboration mechanism, the traditional single-person pursuit behavior is transformed into a group intelligence collaboration model.

[0068] Therefore, the present invention employs the above-mentioned multi-microgrid collaborative scheduling method based on green power balance and time-sharing response, and the beneficial technical effects are as follows:

[0069] (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 economic cost objective functions for multi-microgrid, microgrid, and cluster energy operators through hierarchical and subject-specific construction, a solid model foundation is laid for subsequent scheduling optimization.

[0070] (2) An innovative ENGO algorithm was designed. Based on the traditional NGO algorithm, an elite Northern Goshawk population was introduced. By dynamically learning the global optimal position information, an experience-driven hierarchical search mode was formed. A nonlinear convergence factor was designed so that the search behavior of the elite Northern Goshawk population would be adaptively adjusted with the iteration process. The traditional single-individual pursuit behavior was transformed into group intelligent cooperation through the elite individual collaboration mechanism. These improvements significantly enhanced the performance of the algorithm in multi-interest coordinated optimization.

[0071] (3) A demand-side response model based on time-of-use reference dependence and loss aversion was established. Prospect theory was innovatively introduced to break through the limitations of traditional demand response models. By constructing a dynamic electricity price system for three time periods (peak, flat, and valley), and taking the flat electricity price as the behavioral reference benchmark, a demand-side response model based on relative electricity price reference dependence was established. Combined with loss aversion theory, an asymmetric electricity price adjustment strategy was designed to more accurately characterize the nonlinear perception characteristics of users' electricity price fluctuations.

[0072] (4) A multi-microgrid dispatch model based on green electricity balance factor was established. This model quantitatively assesses the differences in green electricity absorption capacity among microgrids and establishes a cross-microgrid energy trading mechanism. The green electricity allocation factor is innovatively introduced to drive the spatiotemporal redistribution of new energy power generation. The cluster energy operator coordinates and optimizes the three types of transaction costs, which improves the overall economic efficiency while ensuring supply and demand matching, and effectively promotes the absorption of renewable energy and multi-entity collaborative operation.

[0073] Practical application results show that, compared with traditional NGOs, the ENGO proposed in this invention reduces the cost of solution results by 10.15% 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 system energy supply pressure and significantly improves the degree of supply-demand matching. Based on the green electricity balance 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. Attached Figure Description

[0074] Figure 1 This is a flowchart of a multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to the present invention;

[0075] Figure 2 A comparison chart of the total economic costs of multiple microgrids during the optimization and iteration process of NGOs and ENGOs;

[0076] Figure 3 This is a comparison chart of the optimal total economic cost of multi-microgrids for NGOs and ENGOs. Detailed Implementation

[0077] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0078] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0079] Example 1

[0080] like Figure 1 As shown, a multi-microgrid collaborative scheduling method based on green power balance and time-sharing response includes the following steps:

[0081] Step S1: Establish various distributed power source models and demand-side response models in the multi-microgrid. The distributed power source models include photovoltaic equipment models, wind power equipment models, micro gas turbine models, and energy storage system models.

[0082] The power generation capacity of photovoltaic (PV) and wind power equipment is determined by relevant weather characteristics. The parameters and power output constraints of the PV and wind power equipment models are shown below:

[0083] ;

[0084] ;

[0085] in, This indicates the output power of the photovoltaic equipment model; This indicates the output power of the wind turbine model; This indicates the rated output power of the photovoltaic equipment model; This indicates the rated output power of the wind turbine model; , , , These represent the current light intensity, rated light intensity, current ambient temperature, and rated ambient temperature of the photovoltaic equipment model, respectively. This represents the temperature influence coefficient of the photovoltaic equipment model; , , and These represent the current wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the wind power equipment model, respectively.

[0086] Micro gas turbines are important power generation devices in microgrids. The parameters and output constraints of the micro gas turbine model are shown below:

[0087] ;

[0088] in, , These represent the electrical output power of the micro gas turbine model; Indicates the calorific value of natural gas. ; This represents the energy conversion efficiency of a micro gas turbine model; , These represent the lower and upper limits of the output of the micro gas turbine model, respectively.

[0089] Energy storage systems are devices that enable the balanced distribution of green electricity over time, allowing for time-shifting of energy. The parameters of the energy storage system model and the constraints on energy storage charging and discharging are shown below:

[0090] ;

[0091] in, Indicates the energy storage system model in Energy storage at all times; Indicates the energy storage system model in Energy storage at all times; , These represent the charging or discharging amount of the energy storage system model, respectively. , These represent the charging efficiency and discharging efficiency of the energy storage system model, respectively. Indicates the energy storage system model in The amount of charge at any given moment; Indicates the energy storage system model in The amount of discharge at any given moment; This indicates the upper limit of the charging capacity of the energy storage system model; This indicates the lower limit of the charging capacity of the energy storage system model; This indicates the upper limit of the discharge capacity of the energy storage system model; This indicates the lower limit of the discharge capacity of the energy storage system model; This represents the energy stored in the energy storage system model; This indicates the upper limit of the energy storage capacity of the energy storage system model; This represents the lower limit of the energy storage capacity of the energy storage system model.

[0092] User load typically consists of a baseline load and a flexible load. Flexible load represents users' non-urgent electricity demand, which can be guided and adjusted through time-of-use pricing strategies. To more accurately describe user response to electricity consumption, this invention improves the traditional price elasticity matrix based on prospect theory, a key conclusion in economics. Its core lies in reference dependence and loss aversion. Reference dependence refers to user demand adjustment decisions being based on a relative reference price, rather than an absolute value. Based on this, this invention introduces three electricity price periods: peak price period, flat price period, and off-peak price period. The flat price period, serving as a reference price for users, is the longest period relative to the peak and off-peak periods. The flat price period will be adjusted to varying degrees based on the source and load conditions at different times during the peak and off-peak periods. Loss aversion means that the pain of a user for an equivalent amount of loss is 2-2.5 times greater than the pleasure of a gain; therefore, the adjustment amount during the peak price period is usually greater than the adjustment amount during the off-peak period.

[0093] The electricity price-guided demand-side response model, based on time-of-use reference dependence and loss aversion theory, has an electricity price response coefficient. The specific expression is:

[0094] ;

[0095] in, express Time-of-use electricity price Electricity price response coefficient of load response at any given time; Indicates time-of-use electricity pricing Time-of-use electricity price The elasticity coefficient of the load response at any given time; express The difference between the current electricity price and the electricity price during the off-peak period; This indicates the electricity price during the off-peak period. , These represent the minimum and maximum electricity prices, respectively. Indicates the loss aversion coefficient. ; This represents the set of peak electricity price periods; Represents the set of times during which electricity prices are at rest; This represents the set of times during off-peak electricity prices.

[0096] Based on electricity price response coefficient Constructing an electricity price elasticity matrix This allows us to obtain the load response at each time point, specifically expressed as:

[0097] ;

[0098] ;

[0099] in, express The load response at any given time; express Responding to current electricity prices; This represents the load response to a change in electricity price per unit.

[0100] Finally, it is necessary to ensure that the power generation of the microgrid equals the load, that is, to ensure the energy balance constraint of the microgrid. The specific expression is as follows:

[0101] ;

[0102] in, express The electrical output power of the micro gas turbine model at any given time; Indicates the energy storage system model in The amount of discharge at any given moment; and They represent The output power of the photovoltaic equipment model and the wind power equipment model at any given time; Indicates the energy storage system model in The amount of charge at any given moment; express The load demand of the microgrid at all times.

[0103] Step S2: Construct a three-layer hybrid game model for multiple microgrids.

[0104] In response to For a multi-microgrid system composed of individual microgrids, a scheduling model for the upper-level microgrid, a non-cooperative game model between the middle-level microgrid and users, and a cooperative game model based on the green electricity equilibrium allocation factor for the lower-level microgrid are constructed respectively.

[0105] In the upper-level microgrid dispatch model, the economic cost of each microgrid is minimized by independently adjusting the dispatch schemes of each microgrid within the distribution network area. The economic objective function of a single microgrid is... The specific expression is:

[0106] ;

[0107] in, Indicates the first The economic cost of a microgrid under this dispatch scheme; Indicates the cost of natural gas within the system; Indicates carbon trading costs; and These represent the investment and operation and maintenance costs of equipment in a microgrid, respectively. This represents the penalty cost for an imbalance between supply and demand; This represents the cost of grid interaction.

[0108] Each mid-level microgrid-user non-cooperative game model contains two players: the microgrid and the user. The microgrid uses the user's electricity price and the response incentive compensation price as its game strategy, while the user responds accordingly based on the electricity price. The specific expression of the mid-level microgrid-user non-cooperative game model is as follows:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] in, Represents the set of participants; Indicates a microgrid; Indicates user; This indicates the electricity price set for the microgrid; This indicates the user's load response. , These represent the generation cost of the microgrid and the electricity price for users, respectively. This indicates the economic costs associated with the electricity price response mechanism; Indicates equipment adjustment costs; Indicates the first Expression of the mid-level microgrid-user non-cooperative game model for a microgrid.

[0114] Because different microgrids differ in their distributed energy structure, load demand, and climate environment, energy trading can be conducted between them to achieve supply and demand balance and maximize profits. The green energy equilibrium allocation factor can assess the green energy absorption level of each microgrid, and then redistribute the renewable energy generation of multiple microgrids through transmission lines between them to match the environmental and load differences of different microgrids. Inter-microgrid transactions are determined by the cluster energy operator, whose optimization goal is to maximize its own profits. When managing energy, three main transaction costs are considered: transactions between multiple microgrids, transactions between multiple microgrids and the distribution network, and operation and maintenance costs during the transaction process.

[0115] The specific expression of the lower-level cooperative game model based on the green electricity equilibrium allocation factor is as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] in, This represents the modeling of various game players, including cluster energy operators and microgrids; This indicates the revenue of the cluster energy operator; Indicates the benefits of multiple microgrids; This represents the transaction costs between the cluster energy operator and multiple microgrids; This represents the transaction costs between the cluster energy operator and the distribution network; This indicates the cost of transmitting electricity between microgrids; Indicates the first Transaction costs between microgrids and cluster energy operators; Indicates the microgrid transaction price; Indicates microgrid transaction volume; This represents the expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level; Indicates the first The cost of generating electricity in a microgrid; Indicates the number of microgrids that make up the multi-microgrid system;

[0120] Indicates the first The green efficiency benefit of a microgrid, which is negative, can incentivize the microgrid to sell or purchase green electricity. The specific expression is as follows:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] in, express Time of the first The larger the value of the green electricity distribution factor of a microgrid, the more green electricity the microgrid can accommodate. and They represent The output power of the photovoltaic equipment model and the wind power equipment model at any given time; Indicates the energy storage system model in Energy storage at all times; , They represent The weighting of the impact of current source load and energy storage on green electricity absorption capacity; express The load response at any given time; express Time's up The amount of green electricity purchased by the microgrid at any time.

[0126] Total economic cost of multiple microgrids under this dispatch scheme for:

[0127] .

[0128] Step S3: Use the Elite Guided Northern Eagle (ENGO) algorithm to solve the three-layer hybrid game model and output the optimal scheduling scheme for multiple microgrids. Specifically, this includes:

[0129] Step S301: In the upper-level microgrid scheduling model, set the parameters of ENGO, including the maximum number of iterations, the dimension of the optimization variables, and the population size.

[0130] In this model, population location is an optimization variable dimension. In the upper-level microgrid scheduling model, the optimization variables include the unit output schemes of the microgrid at each time point.

[0131] Step S302: Set the correlation coefficients and output constraints for various distributed power generation models in the microgrid. The correlation coefficients are the parameters of the various distributed power generation models described in step S1, such as... , The output constraints include equipment output constraints, energy storage charging and discharging constraints, and energy balance constraints, the formulas of which are explained in detail in step S1.

[0132] Step S303: Use ENGO to search for the optimal operation scheme for each microgrid.

[0133] Initialize the population positions. Calculate the fitness of a single microgrid, which is the economic objective function of that microgrid. Continuously update the population positions iteratively. When ENGO iterates to the maximum number of iterations, output the optimal scheduling scheme for each microgrid under the lowest economic cost, which is the solution result of the upper-level microgrid scheduling model.

[0134] Step S304: In the mid-level microgrid-user non-cooperative game model, input the user load information and the solution results of the upper-level microgrid scheduling model.

[0135] Step S305: Use ENGO to search for user electricity pricing schemes and user load response in the mid-level microgrid-user non-cooperative game model.

[0136] Step S306: Based on the time-sharing reference dependence and loss aversion demand-side response model, adjust the user load response to ensure that the adjustment matches the user's needs and decision-making psychology. Simultaneously, adjust the upper-level dispatch scheme of the microgrid so that the economic cost of the mid-level optimization result is better than the upper-level dispatch result.

[0137] Step S307: When the ENGO iteration reaches the maximum number of iterations, output the optimal solution set that satisfies both the microgrid and the users, which is the scheduling result of the mid-level microgrid-user non-cooperative game model.

[0138] Step S308: In the lower-level cooperative game model based on the green electricity balance allocation factor, input the scheduling results of the middle-level microgrid-user non-cooperative game model, and calculate the green electricity balance allocation factor of each microgrid.

[0139] Step S309: Use ENGO to search and update the energy trading price of the cluster energy operator. Use the purchase price of electricity and the selling price of energy of each microgrid as optimization variables, continuously update and iterate, and combine with the Cplex commercial solver to calculate the optimal trading energy of each microgrid.

[0140] Step S310: When the ENGO iteration reaches the maximum number of iterations, output the optimal solution set that satisfies both the cluster energy operator and the multiple microgrids, that is, the scheduling result of the lower-level cooperative game model based on the green electricity balance allocation factor, and complete the optimal scheduling scheme of the multiple microgrids.

[0141] Among them, ENGO is an improvement on the traditional Northern Goshawk Optimizer (NGO) by introducing an elite Northern Goshawk population, a nonlinear convergence factor, and an elite individual collaboration mechanism.

[0142] NGO is a metaheuristic algorithm that simulates the hunting behavior of northern eagles, and it is mainly divided into two stages: exploration and development.

[0143] First, the NGO initializes the individual locations of Northern Goshawks, which constitute the set of optimization variables. The specific expression is:

[0144] ;

[0145] in, This represents the population in the first iteration. Location of an individual Northern Goshawk; and These represent the vectors of the maximum and minimum population positions, respectively, with dimensions consistent with the number of optimization variables. Represents uniform random numbers, range represents .

[0146] The first stage of the Northern Goshawk location update is identification and attack. The NGO will randomly select prey to attack. This process will involve a global exploration of the variable space to pinpoint the location of the optimal variable. The update formula is:

[0147] ;

[0148] in, Indicates the first The location of the prey; Indicates the interval is Random numbers; Indicates the first In the population of the second iteration Location of an individual Northern Goshawk; Represents a random integer between 1 and 2; Indicates the first In the population of the second iteration Location of an individual Northern Goshawk; Indicates the first In the population of the second iteration The fitness function value corresponding to the location of each individual Northern Goshawk.

[0149] The second stage of the Northern Goshawk location update is the pursuit and escape. In this process, the location is updated by simulating the behavior of the prey escaping and the Northern Goshawk chasing, thereby conducting a local search of the prey's local location.

[0150] However, when solving the three-layer hybrid game model proposed in this invention, NGOs, due to their relatively simple population search strategy, struggle to find optimal trading and scheduling schemes for multiple microgrids. Therefore, this invention proposes ENGOs.

[0151] An elite Northern Goshawk population is introduced into the general population. Through dynamic learning of globally optimal positional information, an experience-driven hierarchical search model is formed. The elite Northern Goshawk population possesses significantly stronger prey-hunting and hunting abilities compared to individual general Northern Goshawks. The update formula for the search phase of the elite Northern Goshawk population is as follows:

[0152] ;

[0153] in, This indicates the optimal individual population position.

[0154] Elite Northern Goshawk populations can learn from the best individuals in the population to hunt, and their hunting skills become increasingly proficient with each iteration. This invention designs a nonlinear convergence factor to simulate this process, enabling the search behavior of the elite Northern Goshawk population to adaptively adjust with the iteration process. This strategy improves the algorithm's early-stage development and later-stage search capabilities. The specific expression is:

[0155] ;

[0156] ;

[0157] in, Indicates the nonlinear convergence factor; Indicates the number of population iterations; This represents the maximum number of iterations for the population.

[0158] In the second phase, an elite individual collaboration mechanism is introduced, transforming the traditional individual hunting behavior into a collective intelligent cooperative model. Elite northern goshawk populations enhance hunting efficiency by cooperating with other individuals. The search formula is:

[0159] ;

[0160] in, Indicates the first In the population of the second iteration Location of an individual Northern Goshawk.

[0161] To verify the effectiveness of this invention, three microgrids from different regions were selected to form a multi-microgrid. For this multi-microgrid, both the traditional NGO algorithm and the ENGO algorithm of this invention were used for optimization, and the total economic cost of the multi-microgrid under these two different algorithms was compared. The experimental results are as follows: Figure 2 and Figure 3 As shown.

[0162] Experimental results show that the ENGO of this invention has significant advantages over 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.

[0163] Therefore, this invention adopts the above-mentioned multi-microgrid collaborative scheduling method based on green power balance and time-sharing response, which solves the supply and demand mismatch caused by high proportion of distributed new energy grid connection conditions and regional differences between different microgrids, optimizes the trading strategy and scheduling scheme of multi-microgrids, realizes the optimal economic operation of multi-microgrids, accurately characterizes the electricity consumption behavior characteristics of users, and provides an innovative technical solution for the optimized scheduling of multi-microgrids.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions 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 collaborative scheduling method based on green power balance and time-sharing response, characterized in that, Includes the following steps: Step S1: Establish various distributed power source models and demand-side response models in multi-microgrids. Distributed power source 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 for multiple microgrids, including an upper-layer microgrid scheduling 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 scheme for multiple microgrids; In the upper-level microgrid dispatch model, the economic objective function of a single microgrid The specific expression is: ； in, Indicates the first The economic cost of a microgrid under this dispatch scheme; Indicates the cost of natural gas within the system; Indicates carbon trading costs; and These represent the investment and operation and maintenance costs of equipment in a microgrid, respectively. This represents the penalty cost for an imbalance between supply and demand; Indicates the cost of grid interaction; The specific expression for the mid-level microgrid-user non-cooperative game model is as follows: ； ； ； ； in, Represents the set of participants; Indicates a microgrid; Indicates user; This indicates the electricity price set for the microgrid; This indicates the user's load response volume; , These represent the generation cost of the microgrid and the electricity price for users, respectively. This indicates the economic costs incurred by the electricity price response policy; Indicates equipment adjustment costs; Indicates the first Expression of the mid-level microgrid-user non-cooperative game model for a microgrid; The specific expression of the lower-level cooperative game model based on the green electricity equilibrium allocation factor is as follows: ； ； ； in, This represents the modeling of various game players, including cluster energy operators and microgrids; This indicates the revenue of the cluster energy operator; Indicates the benefits of multiple microgrids; This represents the transaction costs between the cluster energy operator and multiple microgrids; This represents the transaction costs between the cluster energy operator and the distribution network; This indicates the cost of transmitting electricity between microgrids; Indicates the first Transaction costs between microgrids and cluster energy operators; Indicates the microgrid transaction price; Indicates microgrid transaction volume; This represents the expression of the cooperative game model based on the green electricity equilibrium allocation factor at the lower level; Indicates the first The cost of generating electricity in a microgrid; Indicates the number of microgrids that make up the multi-microgrid system; Indicates the first The green efficiency benefits of a microgrid are specifically expressed as follows: ； ； ； ； in, express Time of the first Green electricity balance allocation factor for microgrids; and They represent The output power of the photovoltaic equipment model and the wind power equipment model at any given time; Indicates the energy storage system model in Energy storage at all times; , They represent The weighting of the impact of current source load and energy storage on green electricity absorption capacity; express The load response at any given time; express Time's up The amount of green electricity purchased by the microgrid at any time This indicates the upper limit of the charging capacity of the energy storage system model; Total economic cost of multiple microgrids under this dispatch scheme for: 。 2. The multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to claim 1, characterized in that, The parameters and power output constraints of the photovoltaic equipment model and the wind power equipment model are shown below: ； ； in, This indicates the output power of the photovoltaic equipment model; This indicates the output power of the wind turbine model; This indicates the rated output power of the photovoltaic equipment model; This indicates the rated output power of the wind turbine model; , , , These represent the current light intensity, rated light intensity, current ambient temperature, and rated ambient temperature of the photovoltaic equipment model, respectively. This represents the temperature influence coefficient of the photovoltaic equipment model; , , and These represent the current wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the wind power equipment model, respectively.

3. The multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to claim 2, characterized in that, The parameters and power output constraints of the micro gas turbine model are shown below: ； in, , These represent the electrical output power of the micro gas turbine model; Indicates the calorific value of natural gas; This represents the energy conversion efficiency of a micro gas turbine model; , These represent the lower and upper limits of the output of the micro gas turbine model, respectively.

4. The multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to claim 3, characterized in that, The parameters of the energy storage system model and the energy storage charge / discharge constraints are shown below: ； in, Indicates the energy storage system model in Energy storage at all times; Indicates the energy storage system model in Energy storage at all times; , These represent the charging or discharging amount of the energy storage system model, respectively. , These represent the charging efficiency and discharging efficiency of the energy storage system model, respectively. Indicates the energy storage system model in The amount of charge at any given moment; Indicates the energy storage system model in The amount of discharge at any given moment; This indicates the upper limit of the charging capacity of the energy storage system model; This indicates the lower limit of the charging capacity of the energy storage system model; This indicates the upper limit of the discharge capacity of the energy storage system model; This indicates the lower limit of the discharge capacity of the energy storage system model; This represents the energy stored in the energy storage system model; This indicates the upper limit of the energy storage capacity of the energy storage system model; This represents the lower limit of the energy storage capacity of the energy storage system model.

5. A multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to claim 4, characterized in that, The electricity price-guided demand-side response model, based on time-of-use reference dependence and loss aversion theory, has an electricity price response coefficient. The specific expression is: ； in, express Time-of-use electricity price Electricity price response coefficient of load response at any given time; Indicates time-of-use electricity pricing Time-of-use electricity price The elasticity coefficient of the load response at any given time; express The difference between the current electricity price and the electricity price during the off-peak period; This indicates the electricity price during the off-peak period. , These represent the minimum and maximum electricity prices, respectively. Indicates the loss aversion coefficient; This represents the set of peak electricity price periods; Represents the set of times during which electricity prices are at rest; This represents the set of times during off-peak electricity prices; Based on electricity price response coefficient Constructing an electricity price elasticity matrix This allows us to obtain the load response at each time point, specifically expressed as: ； ； in, express The load response at any given time; express Responding to current electricity prices; This represents the load response to a unit change in electricity price. Finally, it is necessary to ensure that the power generation of the microgrid equals 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 any given time; Indicates the energy storage system model in The amount of discharge at any given moment; and They represent The output power of the photovoltaic equipment model and the wind power equipment model at any given time; Indicates the energy storage system model in The amount of charge at any given moment; express The load demand of the microgrid at all times.

6. The multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to claim 1, characterized in that, Step S3, which uses ENGO to solve the three-layer hybrid game model, specifically includes: Step S301: In the upper-level microgrid scheduling model, set the parameters of ENGO, including the maximum number of iterations, the dimension of optimization variables, and the population size; Step S302: Set the correlation coefficients and output constraints for various distributed power source models in the microgrid; wherein, the correlation coefficients are the parameters of various distributed power source 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 scheme of each microgrid and calculate the fitness of a single microgrid, i.e., the economic objective function of a single microgrid. When ENGO iterates to the maximum number of iterations, output the optimal scheduling scheme under the lowest economic cost for each microgrid, i.e. the solution result of the upper-level microgrid scheduling model. Step S304: In the mid-level microgrid-user non-cooperative game model, input the user load information and the solution results of the upper-level microgrid scheduling model; Step S305: Use ENGO to search for user electricity pricing schemes and user load response in the mid-level microgrid-user non-cooperative game model respectively; Step S306: Based on the time-sharing reference dependence and loss aversion demand-side response model, adjust the user load response to make the load response conform to the user's needs and decision-making psychology; at the same time, adjust the upper-level dispatch scheme of the microgrid so that the economic cost of the middle-level optimization result is better than that of the upper-level dispatch result. Step S307: When the ENGO iteration reaches the maximum number of iterations, output the optimal solution set that satisfies both the microgrid and the users, i.e., 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 balance allocation factor, input the scheduling results of the middle-level microgrid-user non-cooperative game model, and calculate the green electricity balance allocation factor of each microgrid. Step S309: Use ENGO search to update the energy trading price of the cluster energy operator, use the purchase price of electricity and the selling price of energy of each microgrid as optimization variables, continuously update and iterate, and combine with the Cplex commercial solver to calculate the optimal trading energy of each microgrid. Step S310: When the ENGO iteration reaches the maximum number of iterations, output the optimal solution set that satisfies both the cluster energy operator and the multiple microgrids, that is, the scheduling result of the lower-level cooperative game model based on the green electricity balance allocation factor, and complete the optimal scheduling scheme of the multiple microgrids.

7. A multi-microgrid collaborative scheduling method based on green power balance and time-sharing response according to claim 1, characterized in that, ENGOs, based on traditional NGOs, introduce elite northern goshawk populations, nonlinear convergence factors, and elite individual collaboration mechanisms, specifically manifested as follows: An elite population of Northern Goshawks was introduced into the general population, and an experience-driven hierarchical search model was formed by dynamically learning the global optimal position information. A nonlinear convergence factor was designed to enable the search behavior of the elite Northern Goshawk population to adaptively adjust with the iteration process. By introducing an elite individual collaboration mechanism, the traditional single-person pursuit behavior is transformed into a group intelligence collaboration model.

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