Source network load storage optimization method based on strategy collaborative multi-objective hierarchical optimization

Abstract

The invention discloses a source network load storage optimization method based on strategy cooperation multi-objective hierarchical optimization, and belongs to the technical field of micro-grid optimization configuration. The invention aims to solve the technical problems that the quality of a Pareto solution set and the efficiency of the MOGWO algorithm are influenced and the economy, low-carbon property and reliability of a source network load storage collaborative optimization configuration scheme are synchronously reduced due to the fact that the existing MOGWO algorithm adopts a linearly decreasing convergence factor. The method comprises the following steps: by taking gas turbine unit output, energy storage system charge and discharge power and electric energy storage equipment charge and discharge power as decision variables, constructing an optimization model taking minimization of the total operating cost of the system and minimization of the total carbon emission as double targets, setting constraint conditions, and by utilizing an improved IAMOGWO algorithm, introducing a nonlinear convergence factor dynamic adjustment search strategy, so as to realize the optimal optimization of the total operating cost of the system. Global exploration and local development capabilities are balanced, so that a source-network-load-storage collaborative optimization configuration scheme is synchronously optimized in economy, low-carbon property and reliability. The method is mainly used for source network load storage optimization.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimization configuration technology, specifically to a source-grid-load-storage optimization method based on strategy-coordinated multi-objective hierarchical optimization. Background Technology

[0002] Source-grid-load-storage are the four core physical components of a new power system. "Source" refers to all types of energy conversion equipment; "grid" consists of transmission lines, substations, and distribution stations; "load" refers to the collection of end-user electrical equipment and adjustable resources; and "storage" represents energy storage. Driven by the "dual carbon" goal, the coordinated optimization of source-grid-load-storage has become a key technology for achieving efficient energy utilization and ensuring the safe operation of the system.

[0003] Existing optimization methods use the Multi-objective Grey Wolf Optimizer (MOGWO) algorithm. However, the standard MOGWO algorithm employs a linearly decreasing convergence factor to balance global and local factors. This simple linear strategy is ill-suited to the needs of the MOGWO algorithm at different search stages. In the early stages of iteration, it easily becomes trapped in the local Pareto front; in the later stages, it leads to slow convergence. This affects the quality of the Pareto solution set and the efficiency of the MOGWO algorithm, causing a simultaneous decline in the economy, carbon emissions, and reliability of the source-grid-load-storage coordinated optimization configuration scheme. Therefore, an improved optimization algorithm is needed to overcome these shortcomings. Summary of the Invention

[0004] To overcome the technical problem that the existing MOGWO algorithm uses a linearly decreasing convergence factor, which affects the quality of the Pareto solution set and the efficiency of the MOGWO algorithm, and causes the economy, low carbon emissions and reliability of the source-grid-load-storage coordinated optimization configuration scheme to decline simultaneously, this invention provides a source-grid-load-storage optimization method based on strategy-coordinated multi-objective hierarchical optimization.

[0005] This invention is achieved through the following technical solution:

[0006] A source-grid-load-storage optimization method based on strategy-coordinated multi-objective hierarchical optimization includes:

[0007] S1: The output of the gas turbine unit, the charging and discharging power of the energy storage system, and the charging and discharging power of the electric energy storage device in each time period within the scheduling cycle are used as decision variables. The objective functions are to minimize the total operating cost of the system and minimize the total carbon emissions. The constraints are the electric load constraint, the heat load constraint, the cold load constraint, the equipment operating load constraint, the gas turbine unit ramping constraint, and the power interaction constraint of the large power grid. A multi-objective optimization model is constructed.

[0008] S2: The improved IAMOOGWO algorithm is used to iteratively solve the multi-objective optimization model, outputting an optimal configuration scheme; the nonlinear convergence factor in the improved IAMOOGWO algorithm... The update function is:

[0009] ,

[0010] In the formula, These are the initial and final values ​​of the convergence factor, respectively. Indicates the current iteration number. Indicates the maximum number of iterations. This represents a nonlinear adjustment coefficient greater than 1.

[0011] This invention constructs a dual-objective optimization model for a source-grid-load-storage system that includes various energy devices and constraints. In solving the model, the total operating cost and total carbon emissions of the system are used as dual optimization objectives. Finally, a set of Pareto optimal solutions, i.e., the set of optimal configuration schemes, is obtained.

[0012] Furthermore, the objective function for minimizing the total system operating cost is:

[0013] ,

[0014] In the formula The total operating cost of the system running for 24 hours; The number of micro-sources; For natural gas prices; For micro gas turbine units in Output power during the time period; To improve the power generation efficiency of micro gas turbine units; micro source The power; micro source Maintenance unit cost; This represents the cost coefficient for wind curtailment. This refers to the power output of the wind curtailment. This is the cost coefficient for wasted light. This refers to the power of discarded light. The price at which electricity is purchased from the main power grid; The price at which electricity is sold to the main power grid; This refers to the interaction power between the microgrid and the main power grid; This is the index number of the microsource.

[0015] Furthermore, the objective function for minimizing total carbon emissions is:

[0016] ,

[0017] In the formula, The total cost of treating pollutants generated by the system operating for 24 hours; This represents the types of polluting gases; n is the total number of types of polluting gases. This indicates that the micro gas turbine unit produces polluting gases. The emission coefficient; For micro gas turbine units in Output power during the time period; This indicates that the large power grid produces polluting gases. The emission coefficient; This indicates the amount of electricity purchased from the main power grid; Indicates polluting gases The unit governance cost.

[0018] This invention sets dual optimization objectives, which makes the final optimized decision solution set more diverse, providing decision-makers with a variety of choices to weigh economic and environmental goals.

[0019] Furthermore, the constraint strategy for the constraint conditions includes:

[0020] The electrical load constraint strategy includes: the sum of the electrical power output by each micro-source in the micro-network satisfies the electrical load requirement;

[0021] The heat load constraint strategy includes: the sum of the output heat power of the waste heat boiler, the heat storage tank and the electric heating meets the heat load demand of the park, and the steam generated by the waste heat boiler is given priority to meet the heat load.

[0022] The cooling load constraint strategy includes: the sum of the cooling power output of the lithium bromide absorption chiller and the split air conditioner meets the cooling load requirements of the park.

[0023] The equipment operating load constraint strategy includes: the actual operating power of each micro-source in the microgrid is not less than the minimum technical output power and not more than the rated maximum output power;

[0024] The micro gas turbine unit ramping constraint strategy includes: the power change of the micro gas turbine unit between two adjacent time periods is between the maximum downhill rate and the maximum uphill rate;

[0025] The power constraint strategy for large power grid interaction includes limiting the power interaction of large power grids within a preset range.

[0026] By setting constraints, the model can satisfy the set physical boundaries during runtime.

[0027] Furthermore, the improved IAMOOGWO algorithm iteratively solves the multi-objective optimization model, the process of which includes:

[0028] S21: Initialize the initial wolf pack based on the decision variables, and simultaneously initialize the external archive set;

[0029] S22: Based on the initial wolf pack, objective function, and constraints, obtain the fitness value of each gray wolf in the initial wolf pack;

[0030] S23: Based on the fitness values, perform non-dominated sorting on the individual gray wolves to obtain non-dominated solutions and store them in an external archive set;

[0031] S24: Sort the gray wolf individuals in the external archive set into non-dominant order, retain the solution of the first non-dominant level; then calculate the crowding degree, and select the top three gray wolf individuals as leaders.

[0032] S25: Based on the position of the gray wolf leader and the nonlinear convergence factor, update the position of each gray wolf in the current wolf pack to obtain the next generation wolf pack; iterate through S22-S24 until the preset number of iterations, then execute S26;

[0033] S26: Use the Pareto optimal solution set in the external archive set as the optimal configuration scheme.

[0034] The improved IAMOOGWO algorithm used in this invention uses constraints to verify and correct individual gray wolves during both wolf pack initialization and wolf pack updates, ensuring that the configuration schemes corresponding to individual gray wolves have a feasible basis. At the same time, a nonlinear convergence factor is introduced to balance the global exploration and local development of the algorithm. Furthermore, an external archive set mechanism and an elite retention strategy are introduced to reduce the system's computational overhead.

[0035] Furthermore, after initializing the initial wolf pack, each individual gray wolf is verified and corrected using the aforementioned constraints, including:

[0036] The output of the gas turbine unit and the charging and discharging power of the energy storage system are verified and corrected using the operating load constraints of the aforementioned equipment.

[0037] The charging and discharging power of the energy storage device is verified and corrected using the aforementioned electrical load constraints and equipment operating load constraints.

[0038] In the initialization phase of the wolf pack, this invention verifies and corrects individual gray wolves to ensure that the initial solution of each generated scheme is feasible.

[0039] Furthermore, after each update of the wolf pack's location, the constraints are used to verify and correct each individual gray wolf, including:

[0040] The output of the gas turbine unit is verified and corrected using the equipment operating load constraints and the gas turbine unit ramping constraints.

[0041] The charging and discharging power of the energy storage system is verified and corrected using the operating load constraints of the aforementioned equipment.

[0042] The charging and discharging power of the energy storage device is verified and corrected using the aforementioned electrical load constraints, equipment operating load constraints, and power grid interaction constraints.

[0043] This invention verifies and corrects the individual gray wolves after each iteration of updating the wolf pack's location, ensuring that each updated scheme is feasible.

[0044] Furthermore, when the size of the external archive set exceeds the preset capacity, the individual with the lowest crowding in the current external archive set is removed until the size of the external archive set equals the preset capacity.

[0045] By maintaining the size of the external archive set, the search speed of the algorithm can be kept stable.

[0046] Furthermore, in S23, before storing the non-dominated solution into the external archive set, the dimensions of the non-dominated solution and the external archive set are checked. When the dimensions are inconsistent, zero columns are added to the right side of the side with fewer columns or zero rows are added to the bottom of the side with fewer rows.

[0047] This invention automatically detects the dimensional differences between the external archive set and the newly added non-dominated solutions, and uses zero-padding to make the non-dominated solutions stored in the external archive set mergeable, thus avoiding program interruptions caused by dimensional inconsistencies.

[0048] The beneficial effects of this invention are:

[0049] This invention uses the output of the gas turbine unit, the charging and discharging power of the energy storage system, and the charging and discharging power of the electric energy storage device as decision variables. It constructs an optimization model with dual objectives: minimizing the total system operating cost and minimizing total carbon emissions. Constraints are provided by electrical load, thermal load, cooling load, equipment operating load, gas turbine unit ramp-up constraint, and power interaction constraint with the mains power grid. An improved IAMOOGWO algorithm is then used to solve the model. During the solution process, a nonlinear convergence factor is introduced and automatically updated in each iteration. It maintains a large value in the early stages of the iteration to ensure a wide distribution of the "wolf pack" and enhance the global search; it decreases rapidly in the later stages to make the solution closer to reality and strengthen local optimization. By dynamically adjusting the search strategy through the nonlinear convergence factor, the premature convergence problem caused by the use of linear factors in existing technologies is solved. Simultaneously, the dual objectives and constraints control the initialization and updating of decision variables during the solution process, ensuring that all generated solutions satisfy physical constraints. Furthermore, an external archive set mechanism and an elite retention strategy are introduced to maintain the frontier distribution of the Pareto optimal solution set.

[0050] In summary, the method of this invention effectively balances global exploration and local development capabilities, enabling the source-grid-load-storage collaborative optimization configuration scheme to simultaneously optimize economy, low carbon emissions, and reliability. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 This is an execution flowchart of one embodiment of the method of the present invention;

[0053] Figure 2 This is a schematic diagram of the execution flow of the improved multi-objective gray wolf algorithm (IAMOGWO) of this invention;

[0054] Figure 3 This is a schematic diagram comparing the network loss-iteration count curves of the MOGWO and IAMOOGWO algorithms in one embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the output curves of each distributed power source in the traditional MOGWO algorithm;

[0056] Figure 5 This is a schematic diagram of the output curves of each distributed power source in the IAMOOGWO algorithm proposed in this invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Method 1

[0059] refer to Figure 1 This implementation method is achieved through the following steps:

[0060] Step S100: Select decision variables, objective function and constraints, and construct a multi-objective optimization model.

[0061] Step S101: Use the output of the gas turbine unit, the charging and discharging power of the energy storage system, and the charging and discharging power of the electric energy storage device for each time period within the scheduling cycle as decision variables.

[0062] In this embodiment, the boundary constraints of the decision variables are:

[0063] The output limit of the gas turbine unit is 20 (KW) to 40 (KW);

[0064] The boundary of the charging and discharging power of the energy storage system is: -500 (KW) to 500 (KW), with discharging being positive and charging being negative;

[0065] The boundary between the charging and discharging power of the energy storage device is -20 (KW) to 25 (KW), with discharging being positive and charging being negative.

[0066] The scheduling cycle is 24 hours, divided into 24 time periods, which are numbered as follows: .

[0067] Step S102: The objective functions are to minimize the total operating cost of the system and to minimize the total carbon emissions.

[0068] The objective function for minimizing the total operating cost of the system is:

[0069] ;

[0070] In the formula The total operating cost of the system running for 24 hours; The number of distributed power sources or devices in a microgrid; For natural gas prices; For gas turbine units in Output power during the time period; The power generation efficiency of the gas turbine unit; micro source The power; micro source Maintenance unit cost; This represents the cost coefficient for wind curtailment. This refers to the power output of the wind curtailment. This is the cost coefficient for wasted light. This refers to the power of discarded light. The price at which electricity is purchased from the main power grid; The price at which electricity is sold to the main power grid; This refers to the interaction power between the microgrid and the main power grid; This refers to the index number of distributed power sources or devices in a microgrid.

[0071] Total carbon emissions are reflected in the cost of treating pollutants, so the total cost of treating pollutants generated by the system operating for 24 hours is used to characterize total carbon emissions.

[0072] The objective function for minimizing total carbon emissions is:

[0073] ;

[0074] In the formula, The total cost of treating pollutants generated by the system operating for 24 hours; Indicates the types of polluting gases, including , and n represents the total number of types of polluting gases; This indicates that the gas turbine unit produces polluting gases. The emission coefficient; For gas turbine units in Output power during the time period; This indicates that the large power grid produces polluting gases. The emission coefficient; This indicates the amount of electricity purchased from the main power grid (0 when selling electricity). Indicates polluting gases The unit governance cost.

[0075] The dual objectives of minimizing the total system operating cost and minimizing the total carbon emissions are optimized in parallel with equal weights.

[0076] Step S103: Use electrical load constraints, thermal load constraints, cold load constraints, equipment operating load constraints, gas turbine unit ramping constraints, and power interaction constraints of the large power grid as constraints.

[0077] The electrical load constraint strategy includes ensuring that the sum of the electrical power output from each micro-source in the micro-network meets the electrical load demand. This includes battery charging state constraints and battery discharging state constraints.

[0078] The charging state constraint satisfies the following formula:

[0079] ,

[0080] In the formula, For the wind turbine Predicted output power for the time period; For photovoltaics Predicted output power for the time period; Indicates that the energy storage device is in Charging power during a given period; This indicates the charging efficiency of the energy storage device. for Electricity load demand during a given time period;

[0081] The discharge state constraint satisfies the following formula:

[0082] ,

[0083] In the formula, This represents the discharge power of the energy storage device during time period i; Indicates the discharge efficiency of the energy storage device; This is the multiplication operator.

[0084] The heat load constraint strategy includes: the sum of the output heat power of the waste heat boiler, the heat storage tank and the electric heating meets the heat load demand of the park, and the steam generated by the waste heat boiler is given priority to meet the heat load, and the excess steam can be used for cooling and heat storage.

[0085] When the heat storage tank releases heat, the following formula must be satisfied:

[0086] ,

[0087] In the formula, This refers to the power of the waste heat boiler. Power of the heat storage tank; For heat release efficiency; The heating capacity of the electric heating equipment; To meet heat load requirements; This is a time-period index.

[0088] When the heat storage tank stores heat, the following formula must be satisfied:

[0089] ,

[0090] In the formula, For heat storage efficiency.

[0091] The cooling load constraint strategy includes ensuring that the combined cooling power output of the lithium bromide absorption chiller and the split air conditioner meets the park's cooling load requirements.

[0092] Satisfy the following formula:

[0093] ,

[0094] In the formula, This refers to the power of a lithium bromide refrigerator. This refers to the power output of a split-type air conditioner. This is a cooling load.

[0095] The strategy for constraining the operating load of the equipment includes: the actual operating power of each micro-source in the microgrid is not lower than the minimum technical output power and not higher than the rated maximum output power.

[0096] Satisfy the following formula:

[0097] ,

[0098] In the formula, This represents a micro-source index, used to uniquely identify each distributed power source or device in a microgrid. micro source The actual output electrical power; micro source The minimum output power of the technology; micro source The rated maximum output power.

[0099] The strategy for the gas turbine unit's ramp-up constraint includes: the change in rate between two adjacent time periods is between the maximum downhill rate and the maximum uphill rate; satisfying the following formula:

[0100] ,

[0101] In the formula, Indicates the power output of the gas turbine unit during time period i; express Gas turbine unit power during the time period; This indicates the maximum downhill / uphill rate between two adjacent time periods; This indicates the maximum uphill rate between two adjacent time periods.

[0102] The strategy for constraining the power interaction of the large power grid includes limiting the power interaction within a preset range. This satisfies the formula:

[0103] ,

[0104] In the formula, The lower limit of power for interaction with the main power grid. This refers to the upper limit of power for interaction with the main power grid.

[0105] Step S200: Iteratively solve the multi-objective optimization model using the improved IAMOOGWO algorithm, and output the optimal configuration scheme. The solution process is as follows: Figure 2 As shown, it includes:

[0106] Step S201: Initialize the first generation of wolf packs based on the decision variables, and at the same time initialize an external archive set with empty content.

[0107] Specifically, decision variables at different time periods are mapped to the positions of individual gray wolves, and all gray wolves are considered as the initial wolf pack. Each gray wolf in the pack includes the output of the gas turbine unit, the charging and discharging power of the energy storage system, and the charging and discharging power of the electrical energy storage device.

[0108] After initializing the initial wolf pack, each individual gray wolf is validated and corrected using the aforementioned constraints. The initialization phase validation aims to generate feasible initial solutions with diversity, primarily involving boundary constraint validation of decision variables to ensure that the values ​​of individual gray wolves remain within the limits allowed by the physical equipment. At this stage, constraints such as temporal climbing on individual gray wolves are not validated to maintain the randomness of the initial population. The validation and correction methods include:

[0109] (1) Verify and correct the output of the gas turbine unit using the operating load constraints of the aforementioned equipment:

[0110] Verify whether the actual electrical power output of the gas turbine unit meets the equipment operating load constraints. If it does, proceed; otherwise, forcibly adjust the output power of the gas turbine unit to the nearest feasible boundary value. The formula for forced adjustment is:

[0111] ,

[0112] In the formula, This indicates the output power of the gas turbine unit. This represents the technical minimum output of the gas turbine unit. This refers to the rated maximum output of the gas turbine unit.

[0113] (2) Verify and correct the energy storage system's charging and discharging power by constraining the operating load of the equipment:

[0114] Verify whether the charging and discharging power of the energy storage system meets the equipment operating load constraints. If it does, proceed; otherwise, forcibly adjust the charging and discharging power of the energy storage system to the nearest feasible boundary value. The formula for forced adjustment is:

[0115] ,

[0116] In the formula, This is the maximum charging power of the energy storage system (taken as a positive value, but represented as a negative value in the formula). This represents the maximum discharge power of the energy storage system (take a positive value).

[0117] (3) Verify and correct the charging and discharging power of the energy storage device using the aforementioned electrical load constraints and equipment operating load constraints:

[0118] The charging and discharging power of the energy storage device is verified and corrected using the operating load constraints of the aforementioned equipment.

[0119] Verify whether the charging and discharging power of the energy storage device meets the equipment's operating load constraints. If it does, proceed; otherwise, forcibly adjust the energy storage system's charging and discharging power to the nearest feasible boundary value. The formula for forced adjustment is:

[0120] ,

[0121] In the formula, This is the maximum charging power of the energy storage device (taken as a positive value, but represented as a negative value in the formula). This represents the maximum discharge power of the energy storage device (take a positive value).

[0122] The charging and discharging power of the energy storage device is verified and corrected using the aforementioned electrical load constraints.

[0123] First, update the energy boundary value SOC based on the charging and discharging power of the energy storage device, using the following formula:

[0124] ,

[0125] In the formula, This refers to the rated capacity of the energy storage device.

[0126] Then determine whether the energy boundary value SOC satisfies the electrical load constraint, using the following formula:

[0127] ,

[0128] In the formula, The minimum permissible state of charge, This represents the highest permissible state of charge.

[0129] If the conditions are met, proceed; if not, perform the following checks and adjustments:

[0130] (a) If This indicates that the energy storage device has been overcharged and needs to have its energy usage reduced. The charging and discharging power of the energy storage device is set to... The calculation method is as follows:

[0131] ,

[0132] ,

[0133] ,

[0134] In the formula, Indicates the time step between adjacent time periods. Indicates in The average power that needs to be adjusted during the time period. This is to reduce charging or increase discharging.

[0135] (b) If This indicates that the energy storage device has discharged too much energy and needs to be replenished. The charging and discharging power of the energy storage device is set to... The calculation method is as follows:

[0136] ,

[0137] ,

[0138] ,

[0139] This is to reduce the discharge or increase the charging of energy storage devices.

[0140] In addition to the two correction methods (a) and (b) above, this embodiment provides a simplified correction method of forced zeroing:

[0141] When the energy boundary value SOC of the energy storage device is greater than a preset threshold A or less than a preset threshold B, then a forced This ensures that the initial solution satisfies the constraints. The preset threshold A is 4000, and the preset threshold B is 1000.

[0142] Step S202: Based on the initial wolf pack, objective function, and constraints, obtain the fitness value of each gray wolf in the initial wolf pack.

[0143] Specifically: Individual gray wolves, i.e., decision variables, are substituted into two objective functions. The energy balance of the multi-objective optimization model is constrained using heat load and cold load constraints. Each gray wolf individual obtains two fitness values, representing the total operating cost of the system over 24 hours. Total cost of treating pollutants generated by the system operating 24 hours a day .

[0144] Step S203: Based on the fitness value, perform non-dominated sorting on the gray wolf individuals to obtain non-dominated solutions and store them in the external archive set.

[0145] Before storing the non-dominated solution in the external archive set, the dimensions of the non-dominated solution and the external archive set are checked. If the dimensions are inconsistent, zero columns are added to the right of the side with fewer columns or zero rows are added to the bottom of the side with fewer rows.

[0146] The formula for the zero-padding operation is:

[0147] ,

[0148] In the formula, For external archives, It is a non-dominated solution. , All represent matrices with a value of 0. external archives Non-dominated solutions the number of rows, This refers to the number of decision variables, that is, the number of dimensions (length) of the position vector of each individual gray wolf.

[0149] Step S204: Perform non-dominated sorting on the gray wolf individuals in the external archive set, retain the solutions of the first non-dominated level, and delete the solutions of the other levels; then calculate the crowding degree of the gray wolf individuals in the external archive set, and take the top three gray wolf individuals as leaders.

[0150] If, after filtering solutions at the first non-dominant level, the size of the external archive set exceeds the preset capacity, remove the individual with the lowest crowding density from the current external archive set until the size of the external archive set equals the preset capacity.

[0151] Step S205: Based on the position of the gray wolf leader and the nonlinear convergence factor, update the position of each gray wolf in the current wolf pack to obtain the next generation wolf pack; iterate through S202-S204 until the preset number of iterations, and then execute S206.

[0152] The nonlinear convergence factor The update function is:

[0153] ;

[0154] In the formula, These are the nonlinear convergence factors. Initial and final values, Indicates the current iteration number. Indicates the maximum number of iterations. This represents a nonlinear adjustment coefficient greater than 1.

[0155] In this embodiment, , .

[0156] Nonlinear convergence factor This update method, in the early stages of iteration Nonlinear convergence factor The value decreases slowly, maintaining a large value to enhance global exploration capabilities; in the later stages of iteration... Nonlinear convergence factor The value decreases rapidly, enhancing local development accuracy. This is fundamentally different from the linear decreasing strategy used in the standard MOGWO algorithm, effectively balancing the needs of the algorithm at different search stages.

[0157] After each update of the wolf pack's location, the boundaries and timing of each individual gray wolf are verified and corrected using the aforementioned constraints, including:

[0158] (1) The output of the gas turbine unit is verified and corrected using the equipment operating load constraints and the gas turbine unit ramping constraints:

[0159] The output of the gas turbine unit is verified and corrected using the aforementioned ramp-up constraint.

[0160] Verify whether the change in the gas turbine unit's speed between two adjacent time periods falls between the maximum downhill rate and the maximum uphill rate. If it does, proceed; otherwise, adjust the gas turbine unit's speed according to the maximum ramp rate. The adjustment formula is:

[0161] ,

[0162] In the formula, The power change of the gas turbine between two adjacent time periods, when At that time, take ;Dangdang At that time, take .

[0163] The process of verifying and correcting the output of the gas turbine unit using the operating load constraints of the aforementioned equipment is the same as in the initialization phase, and will not be elaborated here.

[0164] (2) The charging and discharging power of the energy storage system is verified and corrected using the operating load constraints of the equipment, which is the same as the initialization stage and will not be described in detail here.

[0165] (3) The charging and discharging power of the energy storage device is verified and corrected using the electrical load constraints, equipment operating load constraints and power grid interaction constraints.

[0166] The charging and discharging power of the energy storage device is verified and corrected using the aforementioned electrical load constraints and equipment operating load constraints, which is the same as the initialization phase and will not be elaborated here.

[0167] Verify whether the charging and discharging power of the energy storage device is within a preset range of the grid's interactive power. If it meets the requirement, proceed; otherwise, adjust the charging and discharging power of the energy storage device. The adjustment formula is:

[0168] ,

[0169] In the formula, This refers to the active power exchanged between the microgrid and the external main power grid (large power grid).

[0170] It should be noted that heat load constraints and cold load constraints are mainly used for energy balance calculations in microgrid system models. They are used to evaluate the fitness value (total system operating cost) of each individual wolf (i.e., configuration scheme). Total carbon emission control costs These are the conditions that must be met when conducting feasibility studies. In feasibility studies, these conditions are indirectly reflected by affecting the power distribution of micro-sources (such as waste heat boilers, electric heating, and chillers), while the direct verification is of the output boundary (equipment operating load constraints) of each micro-source and the resulting balance of cold, heat, and electrical power.

[0171] Step S206: Use the Pareto optimal solution set in the final external archive set as the optimal configuration scheme set.

[0172] Example

[0173] To verify the beneficial effects of the present invention, a comparative experiment was conducted. The experimental process and results are as follows:

[0174] In a unified microgrid test scenario, the differences in minimum number of iterations, average number of iterations, average time consumption, and optimization rate between the Genetic Algorithm (GA), the Grey Wolf Algorithm (GWO), the Multi-Objective Grey Wolf Algorithm (MOGWP), and the improved multi-objective grey wolf algorithm (IAMOGWO) proposed in this invention are compared. The comparison results are shown in Table 1.

[0175] The minimum number of iterations is the minimum number of iterations required to reach the optimal solution; the average number of iterations is the average number of iterations required to reach the optimal solution; the average time consumed is the average time (in seconds) required to complete one full optimization; and the optimization rate is the ratio of the algorithm that successfully finds the optimal solution.

[0176] Furthermore, the performance of the original multi-objective gray wolf algorithm (MOGWO) and the improved multi-objective gray wolf algorithm (IAMOGWO) proposed in this invention were compared in the microgrid optimization scheduling process. Figure 3 The convergence curves of these two algorithms are shown, where the blue curve is the convergence curve of the original multi-objective gray wolf algorithm (MOGWO), and the red curve is the convergence curve of the improved multi-objective gray wolf algorithm (IAMOGWO) proposed in this invention.

[0177] from Figure 3 It can be seen that the IAMOOGWO algorithm converges faster than the MOGWO algorithm, and the final converged objective function value (network loss) is lower, indicating that it can find a better solution for this optimization objective. Furthermore, as shown in Table 2, the Pareto optimal solution obtained by the IAMOOGWO algorithm is significantly better than the MOGWO algorithm in terms of system operating cost and carbon emissions, further demonstrating the effectiveness of the method of this invention in improving the quality of the Pareto solution set.

[0178] Furthermore, the economic performance of the original Multi-Objective Gray Wolf Algorithm (MOGWO) and the improved Multi-Objective Gray Wolf Algorithm (IAMOGWO) was compared, and the results are shown in Table 2. Table 2 shows that the improved Multi-Objective Gray Wolf Algorithm (IAMOGWO) demonstrates significant advantages in reducing system operating costs and environmental pollution. This makes the improved Multi-Objective Gray Wolf Algorithm (IAMOGWO) an effective method for optimizing the operation of combined cooling, heating, and power (CCHP) microgrids, particularly outstanding in handling complex energy dispatch problems with high-dimensionality, multiple constraints, and multiple objectives.

[0179] Table 1

[0180]

[0181] Table 2

[0182]

[0183] like Figure 4 and Figure 5 The figures show a comparison of the output curves of various distributed power sources (such as gas turbines, wind power, photovoltaics, and energy storage) over 24 hours after optimization using the traditional MOGWO algorithm and the IAMOOWO algorithm of this invention. It can be seen that the method of this invention (…) Figure 5 The optimized power allocation scheme is smoother and more reasonable, effectively reducing drastic power fluctuations and demonstrating better collaborative optimization results.

[0184] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0185] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A source-grid-load-storage optimization method based on strategy-coordinated multi-objective hierarchical optimization, characterized in that, include: S1: The output of the gas turbine unit, the charging and discharging power of the energy storage system, and the charging and discharging power of the electric energy storage device in each time period within the scheduling cycle are used as decision variables. The objective functions are to minimize the total operating cost of the system and minimize the total carbon emissions. The constraints are the electric load constraint, the heat load constraint, the cold load constraint, the equipment operating load constraint, the gas turbine unit ramping constraint, and the power interaction constraint of the large power grid. A multi-objective optimization model is constructed. S2: The improved IAMOOGWO algorithm is used to iteratively solve the multi-objective optimization model, outputting an optimal configuration scheme; the nonlinear convergence factor in the improved IAMOOGWO algorithm... The update function is: ， In the formula, These are the initial and final values ​​of the convergence factor, respectively. Indicates the current iteration number. Indicates the maximum number of iterations. This represents a nonlinear adjustment coefficient greater than 1.

2. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 1, characterized in that, The objective function for minimizing the total operating cost of the system is: ， In the formula The total operating cost of the system running for 24 hours; The number of micro-sources; For natural gas prices; For micro gas turbines in Output power during the time period; The power generation efficiency of micro gas turbines; micro source The power; micro source Maintenance unit cost; This represents the cost coefficient for wind curtailment. This refers to the power output of the wind curtailment. This is the cost coefficient for wasted light. This refers to the power of discarded light. The price at which electricity is purchased from the main power grid; The price at which electricity is sold to the main power grid; This refers to the interaction power between the microgrid and the main power grid; This is the index number of the microsource.

3. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 1, characterized in that, The objective function for minimizing total carbon emissions is: ， In the formula, The total cost of treating pollutants generated by the system operating for 24 hours; This represents the types of polluting gases; n is the total number of types of polluting gases. This indicates that the micro gas turbine produces polluting gases. The emission coefficient; For micro gas turbines in Output power during the time period; This indicates that the large power grid produces polluting gases. The emission coefficient; This indicates the amount of electricity purchased from the main power grid; Indicates polluting gases The unit governance cost.

4. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 1, characterized in that, The constraint strategies for the constraints include: The electrical load constraint strategy includes: the sum of the electrical power output by each micro-source in the micro-network satisfies the electrical load requirement; The heat load constraint strategy includes: the sum of the output heat power of the waste heat boiler, the heat storage tank and the electric heating meets the heat load demand of the park, and the steam generated by the waste heat boiler is given priority to meet the heat load. The cooling load constraint strategy includes: the sum of the cooling power output of the lithium bromide absorption chiller and the split air conditioner meets the cooling load requirements of the park. The equipment operating load constraint strategy includes: the actual operating power of each micro-source in the microgrid is not less than the minimum technical output power and not more than the rated maximum output power; The micro gas turbine ramping constraint strategy includes: the change in power of the micro gas turbine between two adjacent time periods is between the maximum downhill rate and the maximum uphill rate; The power constraint strategy for large power grid interaction includes limiting the power interaction of large power grids within a preset range.

5. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 1, characterized in that, The improved IAMOOGWO algorithm iteratively solves the multi-objective optimization model, and the process includes: S21: Initialize the initial wolf pack based on the decision variables, and simultaneously initialize the external archive set; S22: Based on the initial wolf pack, objective function, and constraints, obtain the fitness value of each gray wolf in the initial wolf pack; S23: Based on the fitness values, perform non-dominated sorting on the individual gray wolves to obtain non-dominated solutions and store them in an external archive set; S24: Sort the gray wolf individuals in the external archive set into non-dominant order, retain the solution of the first non-dominant level; then calculate the crowding degree, and select the top three gray wolf individuals as leaders. S25: Based on the position of the gray wolf leader and the nonlinear convergence factor, update the position of each gray wolf in the current wolf pack to obtain the next generation wolf pack; iterate through S22-S24 until the preset number of iterations, then execute S26; S26: Use the Pareto optimal solution set in the external archive set as the optimal configuration scheme.

6. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 5, characterized in that, After initializing the initial wolf pack, each individual gray wolf is verified and corrected using the aforementioned constraints, including: The output of the gas turbine unit and the charging and discharging power of the energy storage system are verified and corrected using the operating load constraints of the aforementioned equipment. The charging and discharging power of the energy storage device is verified and corrected using the aforementioned electrical load constraints and equipment operating load constraints.

7. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 5, characterized in that, After each update of the wolf pack's location, the constraints are used to verify and correct each individual gray wolf, including: The output of the gas turbine unit is verified and corrected using the equipment operating load constraints and the gas turbine unit ramping constraints. The charging and discharging power of the energy storage system is verified and corrected using the operating load constraints of the aforementioned equipment. The charging and discharging power of the energy storage device is verified and corrected using the aforementioned electrical load constraints, equipment operating load constraints, and power grid interaction constraints.

8. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 5, characterized in that, When the size of the external archive set exceeds the preset capacity, remove the individual with the lowest crowding in the current external archive set until the size of the external archive set equals the preset capacity.

9. The source-grid-load-storage optimization method based on strategy-cooperative multi-objective hierarchical optimization according to claim 5, characterized in that, In S23, before storing the non-dominated solution into the external archive set, the dimensions of the non-dominated solution and the external archive set are checked. When the dimensions are inconsistent, zero columns are added to the right of the side with fewer columns or zero rows are added to the bottom of the side with fewer rows.

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