Micro-grid dispatching method, device and equipment considering green certificate-carbon combination and medium

Abstract

The present application provides a kind of microgrid scheduling method, device, equipment and medium considering green certificate-carbon combination, belong to microgrid optimization scheduling technical field, its method includes: obtaining the operating parameter of microgrid system;Based on operating parameter, construct green certificate-carbon combined transaction model;Based on operating parameter and green certificate-carbon combined transaction model, with the minimum system total operating cost containing green certificate-carbon combined transaction cost and flexible load scheduling compensation cost as target, construct objective function, and set constraint condition;Optimal solution is solved to the objective function under the constraint condition using improved Harris eagle optimization algorithm, to output target scheduling scheme;According to target scheduling scheme, each distributed power supply in microgrid system, energy storage system and flexible load are executed scheduling control.The present application can effectively reduce microgrid system operating cost, reduce carbon emissions and improve new energy consumption rate through the collaborative optimization of green certificate-carbon combined transaction mechanism and flexible load.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimization scheduling technology, specifically to a microgrid scheduling method, device, equipment, and medium that takes into account green certificate-carbon co-operation. Background Technology

[0002] Microgrids, as energy supply units integrating distributed power sources, energy storage systems, and loads, play a crucial role in promoting renewable energy consumption and reducing carbon emissions. Carbon emission trading mechanisms constrain system carbon emissions by setting carbon emission quotas, while green electricity certificate trading mechanisms incentivize renewable energy consumption by issuing green certificates for renewable energy generation; both are market-based tools guiding low-carbon operations. Existing microgrid dispatching technologies include methods that optimize distributed power source output to achieve economical operation. For example, related technologies employ improved intelligent optimization algorithms to optimize the output power of each microgrid, aiming to minimize the sum of the economic operating cost and environmental pollution treatment cost of distributed power sources.

[0003] However, such technologies have the following shortcomings: First, the objective function only covers fuel costs, operation and maintenance costs, and conventional pollutant treatment costs, without including carbon trading costs and green certificate trading costs, and cannot guide the system to actively reduce carbon emissions through market-based pricing mechanisms; Second, the scheduling objects are limited to the power supply side, making it difficult to utilize load-side resources to smooth out fluctuations in renewable energy output, which restricts the improvement of system operation flexibility and renewable energy absorption capacity; Third, existing algorithms have limitations in terms of solution accuracy and convergence speed, making it difficult to achieve efficient optimization of the global optimal solution under multiple constraints.

[0004] Therefore, there is an urgent need for a microgrid dispatching method to effectively integrate market-based trading mechanisms with load-side regulation resources within the existing microgrid dispatching framework, so as to reduce system operating costs, improve the efficiency of renewable energy consumption, and effectively control carbon emission levels. Summary of the Invention

[0005] In view of this, it is necessary to provide a microgrid dispatching method, device, electronic equipment and storage medium that takes into account green certificate-carbon linkage, so as to solve the technical problems of the lack of flexible load-side response means in the face of the volatility and intermittency of renewable energy output, resulting in high operating costs, low renewable energy absorption efficiency and poor carbon emission control effect.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides a microgrid dispatching method that considers green certificate-carbon co-operation, comprising: The operating parameters of the microgrid system are obtained, including distributed generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads. Based on the aforementioned operating parameters, a green certificate-carbon joint trading model is constructed, which is used to establish the coupling and linkage relationship between green certificate trading and carbon trading. Based on the operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost including green certificate-carbon joint trading costs and flexible load scheduling compensation costs, an objective function for optimized scheduling is constructed, and constraints including power balance constraints, output constraints of each micro-source, and adjustment capability constraints of the flexible load are set. An improved Harris Eagle optimization algorithm is used to find the optimal solution for the objective function under the constraints, so as to output the target scheduling scheme; According to the target scheduling scheme, scheduling control is performed on each distributed power source, energy storage system and flexible load in the microgrid system.

[0007] In one possible implementation, the flexible load includes at least one or more of the following: a load that can be reduced, a load that can be shifted, and a load that can be transferred. The flexible load scheduling compensation cost is calculated based on the scheduling volume of various types of flexible loads and the corresponding unit compensation price. The constraints on the adjustment capability of the flexible load include at least: the maximum number of reductions and continuous reduction time constraints of the load that can be reduced, the transferable time period constraints of the load that can be transferred, and the upper and lower limits of the transfer power and the total balance constraints before and after the transfer of the load that can be transferred.

[0008] In one possible implementation, the construction of the green certificate-carbon joint trading model includes: Quantify the carbon emission reductions generated by new energy power generation relative to the benchmark thermal power generation; The carbon emission reductions will be used as the carbon emission credit limit for green certificates in the carbon trading market, thus establishing a coupling and linkage between green certificate trading and carbon trading.

[0009] In one possible implementation, the construction of the green certificate-carbon joint trading model includes: The net revenue or net cost of green certificate trading is determined based on the difference between the actual renewable energy generation of the microgrid and the preset quota requirements. The net revenue or net cost of carbon trading is determined based on the difference between the actual carbon emissions of the microgrid system and the free carbon emission allowances. Based on the carbon emission reductions generated by new energy power generation relative to the benchmark thermal power generation, the net revenue from the green certificate trading is coupled into the calculation of carbon trading costs to update the net revenue or net cost of the carbon trading.

[0010] In one possible implementation, the improved Harris Eagle optimization algorithm is used to solve the objective function for the optimal solution under the constraints, in order to output the target scheduling scheme, including: The population is initialized using the Tent chaotic mapping to generate an initial candidate solution set that satisfies the constraints. During the iterative optimization process, the objective function is used as the fitness evaluation benchmark. The nonlinear escape energy update formula and the nonlinear jump strength strategy are used to balance global search and local exploitation. The initial candidate solution set is iteratively updated under the constraints. In the later stages of the iteration, an adaptive Gaussian-Cauchy mutation operation is introduced to perturb and update the current best individual. The quality of the perturbed individual is evaluated based on the objective function. After the iteration termination condition is met, the target scheduling scheme that optimizes the objective function value is output from the final candidate solution set.

[0011] In one possible implementation, the introduction of an adaptive Gaussian-Cauchy mutation operation to perturb and update the optimal individual includes: By using a weight coefficient that varies with the number of iterations, the Gaussian mutation operator and the Cauchy mutation operator are combined in a weighted manner to apply a perturbation to the current best individual and generate a new individual after mutation. The greedy criterion is used to compare the fitness values ​​of individuals before and after mutation, and individuals with better fitness are retained for the next iteration.

[0012] In one possible implementation, the nonlinear escape energy update formula is:

[0013] in, E The escape energy for the current iteration. The initial escape energy, t This represents the current iteration number. T This represents the maximum number of iterations. The nonlinear jump strength strategy is as follows:

[0014] in, J The jumping strength of the prey, t This represents the current iteration number. T The maximum number of iterations, r It is a random number between [0, 1].

[0015] On the other hand, the present invention also provides a microgrid dispatching device that takes into account green certificate-carbon co-operation, comprising: The operating parameter acquisition module is used to acquire the operating parameters of the microgrid system. The operating parameters include distributed power generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads. The linkage model construction module is used to construct a green certificate-carbon joint trading model based on the operating parameters. The green certificate-carbon joint trading model is used to establish a coupling and linkage relationship between green certificate trading and carbon trading. The constraint generation module is used to construct an objective function for optimizing scheduling based on the operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost, including the cost of green certificate-carbon joint trading and the cost of flexible load scheduling compensation. The module also sets constraints including power balance constraints, output constraints of each micro-source, and the adjustment capability constraints of the flexible load. The scheduling scheme determination module is used to solve the objective function under the constraints using an improved Harris Eagle optimization algorithm to output the target scheduling scheme. The scheduling scheme execution module is used to perform scheduling control on each distributed power source, energy storage system and flexible load in the microgrid system according to the target scheduling scheme.

[0016] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the microgrid scheduling method considering green certificate-carbon linkage as described in any of the above implementations.

[0017] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the microgrid scheduling method considering green certificate-carbon integration described in any of the above implementations.

[0018] The beneficial effects of this invention are as follows: The microgrid scheduling method considering green certificate-carbon linkage provided by this invention provides a data foundation for subsequent optimized scheduling by acquiring microgrid operating parameters; it constructs a green certificate-carbon joint trading model, establishing a coupling and linkage relationship between green certificate trading and carbon trading, enabling the system to guide low-carbon operation through market-based price signals; it constructs an objective function and sets constraints with the goal of minimizing the total system operating cost, including green certificate-carbon joint trading costs and flexible load scheduling compensation costs, to achieve multi-objective comprehensive optimization; it uses an improved Harris Eagle optimization algorithm to solve the problem, improving the optimization efficiency and solution accuracy under complex constraints; and it executes scheduling control according to the target scheduling scheme to optimize and adjust the system operating state, thereby effectively reducing system operating costs and improving the efficiency of new energy consumption. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic flowchart of an embodiment of the microgrid dispatching method considering green certificate-carbon linkage provided by the present invention; Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of S102; Figure 3 For the present invention Figure 1 A schematic diagram of another embodiment of S102; Figure 4 For the present invention Figure 1 A schematic diagram of an embodiment of S104; Figure 5 For the present invention Figure 4 A schematic diagram of an embodiment of S403; Figure 6 This is a comparison curve of convergence of different optimization algorithms in one embodiment of the present invention; Figure 7 This is a schematic diagram comparing the iteration curves of the improved Harris Eagle optimization algorithm with four other conventional algorithms in one embodiment of the present invention. Figure 8 A schematic diagram of an embodiment of the microgrid dispatching device considering green certificate-carbon linkage provided by the present invention; Figure 9 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0021] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0022] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] This invention provides a microgrid dispatching method, apparatus, electronic device, and storage medium that considers green certificate-carbon co-location. The technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] Figure 1 This is a schematic flowchart of an embodiment of the microgrid dispatching method considering green certificates and carbon co-operation provided by the present invention, as shown below. Figure 1 As shown, the microgrid dispatching methods that take into account green certificate-carbon co-operation include: S101. Obtain the operating parameters of the microgrid system, including distributed generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads.

[0026] Specifically, the parameters of distributed power sources mainly include the rated capacity, upper and lower limits of output, operation and maintenance cost coefficient, and investment depreciation cost coefficient of wind turbine generators, photovoltaic generators, diesel generators, and energy storage batteries.

[0027] Among them, wind turbines and photovoltaic generators, as renewable energy power generation units, are affected by natural conditions, exhibiting intermittent and fluctuating power output. Diesel generators, as controllable power sources, can provide stable power output when needed, serving as regulation and backup. Energy storage batteries are used to store energy when there is a power surplus and release energy when there is a power shortage, playing a role in peak shaving, valley filling, and smoothing power fluctuations.

[0028] It should be noted that user loads are categorized into two types based on their electricity consumption characteristics: uncontrollable loads and controllable flexible loads. Uncontrollable loads refer to loads whose electricity consumption time and power cannot be adjusted, such as basic lighting and power supply for critical equipment. Flexible loads refer to loads that can be adjusted according to the operational needs of the power system, including transferable loads, loads that can be reduced, and loads that can be shifted. These flexible loads can adjust their electricity consumption time and power within a certain range, providing demand-side response capability to the system.

[0029] It should be noted that all the aforementioned distributed power sources, energy storage systems, and user loads are interconnected through the microgrid's internal distribution network. The microgrid is connected to the upper-level power grid through a point of common coupling, enabling bidirectional power exchange with the external power grid. When the microgrid's internal power generation exceeds the load demand, the excess electricity can be sold to the upper-level power grid; when internal power generation is insufficient, electricity can be purchased from the upper-level power grid to make up the gap.

[0030] The parameters for the green certificate trading market include the green certificate price and the proportion of renewable energy generation quotas. The parameters for the carbon trading market include the carbon trading market price, free carbon emission allowances, and the carbon emission intensity of various power sources. The parameters for time-of-use electricity pricing include the purchase price and sales price of electricity during peak, off-peak, and valley periods.

[0031] In some embodiments of the present invention, the flexible load includes at least one or more of the following: a load that can be reduced, a load that can be shifted, and a load that can be transferred. The compensation cost for flexible load dispatching is calculated based on the dispatching volume of various types of flexible loads and the corresponding unit compensation price; The constraints on the adjustment capability of flexible loads include at least: the maximum number of reductions and continuous reduction time constraints of loads that can be reduced, the transferable time period constraints of loads that can be transferred, and the upper and lower limits of transferable power and the total balance constraints before and after transfer of loads that can be transferred.

[0032] Furthermore, reduceable loads refer to loads whose power consumption can be moderately adjusted within a short period of time. The power demand of such loads is typically variable, such as adjustable-power air conditioning equipment and lighting fixtures whose quantity can be changed as needed. The mathematical model for reduceable loads is shown in equation (1): (1); In the formula: This indicates that after participating in the scheduling, Power at any given moment; Before participating in scheduling Power at any given moment; For its in The amount of power reduction at any given time; using 0-1 variables. This indicates that the load can be reduced for a certain period of time. The state of reduction Indicates that it is in Time is being reduced; for The load reduction factor at any given time. .

[0033] Compensation costs for load reduction Determined using equation (2): (2); In the formula: To reduce compensation costs for load dispatching; Compensation for active power reduction per load unit.

[0034] Furthermore, shiftable loads refer to loads whose power consumption curves can be moved as a whole over time. These loads mainly include industrial users whose production processes have some flexibility and who can adjust work shifts, as well as household appliances such as washing machines, sterilizers, and dishwashers. Power dispatching agencies can implement demand-side management strategies by signing agreements or establishing tacit understandings with these users.

[0035] To ensure the rationality and effectiveness of load shifting, this embodiment establishes constraints on the shiftable time periods. Let the shiftable time period interval within the scheduling cycle be... The duration of the load that can be shifted is As shown in equation (3): (3); in and These are the start and end times before the movable load participates in the scheduling process, respectively.

[0036] Then the set of starting time periods can be shifted. As shown in equation (4): (4); The compensation cost after load shifting is as described in equation (5): (5); In the formula: Compensation costs for load shifting; This represents the initial moment at which the load can be moved after translation. The acceptable shift period for the load; The compensation cost for shifting the active power per unit of load; Original interval Internal t Active power of the transferable load at any given time; 0-1 variable This indicates the translation state of the load. Indicates load from Time period shifted to Time period This indicates that the load has not been shifted.

[0037] Furthermore, transferable load refers to a load whose power consumption intensity and duration can be freely adjusted while maintaining a constant total power consumption. Assume the transfer time interval for transferable load is... The compensation cost after load transfer scheduling is shown in equation (6): (6); in, This represents the compensation price per unit power load transfer; a 0-1 variable. Indicates the transfer status of transferable loads. Indicates load transfer to time, This indicates that the load has not been transferred. time; Indicates its transfer to Active power at any given moment.

[0038] S102. Based on the operating parameters, construct a green certificate-carbon joint trading model. The green certificate-carbon joint trading model is used to establish the coupling and linkage relationship between green certificate trading and carbon trading.

[0039] Specifically, due to its significant advantages, the Green Electricity Certificate (GET) trading mechanism is incorporated into the low-carbon economic dispatch scheme of microgrids in this embodiment. For microgrids that meet their renewable energy quotas, green certificates are sold on the green certificate trading market based on the actual power generation of all renewable energy sources within the microgrid; while microgrids that fail to meet their quotas need to purchase green certificates on the market to achieve quota balance. That is, the green certificate trading mechanism determines the purchase cost or sales revenue of green certificates based on the difference between the actual renewable energy power generation of the microgrid and the preset quota requirements; the carbon trading mechanism determines the purchase cost or sales revenue of carbon allowances based on the difference between the actual carbon emissions of the microgrid system and the free carbon emission allowances.

[0040] In a specific example, the mathematical model shown in equation (7) is used to determine the green certificate transaction cost: (7); In the formula: and These represent the number of green certificates obtained by the system and the number of green certificates that meet the quota requirements, respectively. and They are respectively t Time of the first The actual power generation capacity of renewable energy sources and the actual demand for system electricity load; The proportion of renewable energy power generation quota is preferably 0.2 in this embodiment; and These are the price of green certificates and the proceeds from green certificate trading, respectively. This represents the total number of time periods in a scheduling cycle.

[0041] Based on this, by quantifying the carbon emission reductions generated by new energy power generation relative to benchmark thermal power, the two trading mechanisms are linked, so that the new energy power generation represented by green certificates can offset the corresponding carbon emission rights in the carbon trading market, thereby realizing the joint interaction between green certificate trading and carbon trading.

[0042] As an alternative approach, microgrid systems treat electricity purchased from the upstream grid as generated by coal-fired power units, with the main carbon emission sources being the upstream grid and diesel generator sets. The total carbon allowance for the system is determined by multiplying the electricity generated by the diesel generator sets and the upstream grid by the corresponding free carbon emission allowance. The actual carbon emissions of the system are determined by multiplying the actual electricity generated by the diesel generator sets and the upstream grid by the corresponding carbon emission intensity. Carbon trading costs are calculated by multiplying the difference between actual carbon emissions and free carbon emission allowances by the carbon trading market price. Green certificate trading costs are calculated by multiplying the difference between actual renewable energy generation and the allowance requirement by the green certificate price.

[0043] It should be noted that the carbon trading mechanism allows the buying and selling of carbon emission rights in the market, which is a way of commodifying carbon emission rights. The purpose of this mechanism is to promote the optimization of the energy structure and the improvement of energy efficiency, so as to achieve the goal of reducing greenhouse gas emissions.

[0044] Each carbon-emitting entity is allocated a certain number of carbon emission allowances. If an entity's actual emissions are lower than its allocated allowances, it can sell the remaining allowances on the carbon market; if emissions exceed the allowances, the excess must be purchased on the market.

[0045] In this embodiment, the electricity purchased from the upstream power grid is all generated by coal-fired power units, so the main sources of carbon emissions are the upstream power grid and diesel engines. The total carbon quota model is shown in equation (8): (8) The specific carbon emission allowances for each part are shown in equation (9): (9) In the formula: Carbon emission allowance per unit of electricity generated by diesel generators; Carbon emission allowance per unit of electricity generated from coal-fired power purchased from the upper-level power grid; for The power that the microgrid purchases from the upstream power grid at any given time; T This refers to the settlement cycle for carbon trading fees.

[0046] The actual carbon emission model can be represented by equation (10): (10) In the formula: and These are the actual carbon emissions from diesel engines and the upstream power grid, respectively. Carbon emission intensity per unit of electricity generated by a diesel generator; Carbon emission intensity of thermal power plants purchasing electricity from the upper-level power grid; This represents the total actual carbon emissions of the system.

[0047] The system carbon trading cost model based on the carbon trading mechanism is shown in equation (11): (11) In the formula: For carbon trading costs; A positive result indicates that carbon emissions have exceeded the limit, and carbon emission credits must be purchased. A negative value indicates that revenue was generated from the sale of carbon emission credits; This is the carbon trading market price for that day.

[0048] Furthermore, this embodiment achieves the joint interaction of the two mechanisms based on market-guided transaction prices, demand, and other factors. The carbon emission reduction caused by green certificates is obtained by comparing the carbon emissions generated by coal-fired power generation and new energy power generation, as shown in equation (12): (12) In the formula: Carbon emissions offset by green certificates; Carbon emission reductions caused by renewable energy power generation; , These represent the carbon emissions generated by coal-fired power and renewable energy sources, respectively.

[0049] At this point, the carbon trading cost is as shown in equation (13): (13) In the formula: To systematically consider the carbon trading costs when green certificate trading and carbon trading are combined.

[0050] S103. Based on operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost including green certificate-carbon joint trading costs and flexible load scheduling compensation costs, construct an objective function for optimized scheduling, and set constraints including power balance constraints, output constraints of each micro-source, and adjustment capability constraints of flexible loads.

[0051] Specifically, the total operating cost of the system includes three main parts: conventional operating costs, green certificate-carbon joint trading costs, and flexible load dispatch compensation costs.

[0052] The conventional operating costs mainly include system investment depreciation costs, operation and maintenance costs, fuel costs, and transaction costs for purchasing and selling electricity with the external power grid. The green certificate-carbon joint trading cost is determined based on the aforementioned green certificate-carbon joint trading model and reflects the system's net expenditure or net gain from participating in the green certificate trading market and the carbon trading market. The flexible load dispatch compensation cost is used to quantify the compensation fees required for the system to call upon flexible load resources.

[0053] In a specific example, the total system cost is shown in equation (14): (14) In the formula: , , , , , , These are system depreciation costs, operation and maintenance costs, fuel costs, electricity purchase and sale revenue / costs, carbon trading costs, green certificate trading revenue / costs, and flexible load compensation costs.

[0054] Furthermore, the system investment depreciation cost is shown in equation (15): (15) In the formula: , , , These are the investment depreciation costs for photovoltaic cells, wind turbines, diesel engines, and storage batteries, respectively.

[0055] The investment depreciation cost of photovoltaic cells is shown in equation (16): (16) In the formula: The unit capacity installation cost of photovoltaic power; This is the capacity factor for photovoltaics; The annual interest rate; For photovoltaic lifespan; For photovoltaic cells Power output at all times.

[0056] The investment depreciation cost of the wind turbine is shown in equation (17): (17) In the formula: The unit capacity installation cost of the wind turbine; This is the capacity factor of the fan; For the lifespan of the wind turbine; For wind turbine Actual output power at any given time.

[0057] The investment depreciation cost of the diesel engine is shown in equation (18): (18) In the formula: The unit capacity installation cost of the diesel engine; This is the capacity factor for the diesel engine; For the lifespan of the diesel engine; diesel generator Output power at any given moment.

[0058] The investment depreciation cost of the storage battery is shown in equation (19): (19) In the formula: This refers to the cost coefficient per unit of battery power loss. and They are respectively The charging and discharging power of the battery at all times; The cost of replacing the battery; The total charge and discharge capacity of a battery over its entire lifespan is related to its rated capacity, depth of charge and discharge, and number of charge and discharge cycles. Both charging and discharging incur costs.

[0059] Furthermore, the operation and maintenance costs are shown in equation (20): (20) In the formula: , , , These are the operating and maintenance costs of photovoltaic cells, wind turbines, diesel engines, and batteries, respectively.

[0060] The operation and maintenance cost of photovoltaic cells is shown in equation (21): (twenty one) In the formula: This represents the unit operation and maintenance cost coefficient for photovoltaic systems.

[0061] The operation and maintenance cost of the wind turbine is shown in equation (22): (twenty two) In the formula: This is the unit operation and maintenance cost coefficient for wind turbines.

[0062] The operating and maintenance costs of the diesel engine are shown in equation (23): (twenty three) In the formula: This is the unit operating and maintenance cost coefficient for diesel engines.

[0063] The operation and maintenance cost of the battery is shown in equation (24): (twenty four) In the formula: This is the unit operation and maintenance cost coefficient for batteries.

[0064] Furthermore, the fuel cost is mainly generated by the diesel generator, and its fuel cost is shown in equation (25): (25) in, Fuel cost for diesel generators; diesel generator Output power at any given moment; , , The coefficient for the fuel cost of the diesel generator is preferably taken in this embodiment. , , .

[0065] Furthermore, the revenue / cost of purchasing and selling electricity is shown in equation (26): (26) In the formula: , They are respectively The electricity price and power consumption purchased from the external power grid at all times; , They are respectively The electricity price and power output are constantly being supplied to the power grid.

[0066] Carbon cost trading can be referenced in the aforementioned formula (13), and green certificate trading revenue / cost can be referenced in the aforementioned formula (7). To avoid repetition, it will not be repeated here.

[0067] Furthermore, the cost of flexible load compensation is shown in equation (27): (27) In the formula: The total compensation cost for scheduling flexible loads.

[0068] Furthermore, this embodiment sets the constraints for the operation of the microgrid system based on the above parameters.

[0069] The constraints include power balance constraints, output constraints of each micro-source, operation constraints of the energy storage system, and adjustment capability constraints of flexible loads.

[0070] Furthermore, the power balance constraint requires that at any given moment, the sum of the output of each distributed power source, the charging and discharging power of the energy storage system, and the power exchanged with the external power grid equals the load demand, expressed by equation (28): (28) In the formula, , , and These are the system's base load (not involved in scheduling), shiftable load, reduceable load, and transferable load. Power at any given moment.

[0071] Furthermore, the output constraints of each micro-source include the upper and lower limits of output for photovoltaic, wind turbine, and diesel engine, as well as the ramp rate constraint for diesel engine, which are expressed by equations (29), (30), and (31): (29) (30) (31) In the formula: , These represent the upper and lower limits of photovoltaic power output; , These are the upper and lower limits of the fan's output, respectively; , These are the upper and lower limits of the diesel engine's output, respectively. The diesel engine ramp rate constraint is expressed by equation (32): (32) In the formula: and These represent the downhill and uphill ramp rates of the diesel engine's power generation, respectively.

[0072] Furthermore, the operational constraints of the energy storage system include battery charging and discharging power constraints, remaining capacity constraints, and mutual exclusion constraints of charging and discharging states.

[0073] The power transmission constraints of the tie lines between the system and the main power grid are expressed by equations (32), (33), and (34): (33) (34) (35) In the formula: and These are the electricity purchase and sales status parameters, both of which are binary variables. , Time indicates in The system is in a state of purchasing electricity from the grid. , Time indicates in The system is currently in a state of selling electricity to the power grid.

[0074] Among them, the battery-related constraints are expressed by equations (36), (37), (38), and (39): (36) (37) (38) (39) In the formula: , These are the minimum and maximum remaining battery capacity, respectively. and These are the maximum charging and discharging power of the battery, respectively. and These are all binary variables representing the charging and discharging states of the battery, used to prevent the battery from charging and discharging at the same time. , The time indicates that the battery is in Always in a charging state. , The time indicates that the battery is in It is constantly in a state of discharge; and These represent the battery's capacity at the beginning and end of the cycle, respectively.

[0075] Furthermore, the adjustment capacity constraints of flexible loads are set separately according to the response characteristics of various types of flexible loads, in order to ensure that the call of flexible loads during the scheduling process does not exceed their physical adjustability range.

[0076] To ensure user satisfaction, it is necessary to limit the minimum and maximum continuous reduction time and the number of reductions. In this embodiment, the load reduction-related constraints are expressed by equations (40), (41), and (42): (40) (41) (42) In the formula: Minimum continuous reduction time; This is the maximum continuous reduction time; This represents the maximum number of reductions.

[0077] Among them, the transferable load related constraints are expressed by equations (43) and (44): (43) (44) In the formula: , These are the minimum and maximum values ​​of the transferable load power, respectively. and These represent the power transferred into and out of the transferable load, respectively. This represents the set of working periods for transferable loads.

[0078] It should be understood that without setting limits on the load transfer process, the load may be distributed across multiple independent time periods, resulting in frequent start-ups and shutdowns of the equipment. Therefore, the minimum continuous operating time of the transferred load must be limited, as shown in equation (45): (45) In the formula: This is the minimum continuous running time.

[0079] S104. The improved Harris Eagle optimization algorithm is used to find the optimal solution of the objective function under the constraints, so as to output the target scheduling scheme.

[0080] Among them, the Harris Hawk Optimization (HHO) algorithm is a metaheuristic optimization algorithm that simulates the hunting behavior of Harris Hawks. It achieves the optimization process by simulating the large-scale search of the hawk flock in the exploration phase and the precise attack in the development phase.

[0081] This embodiment makes several improvements to the standard Harris Eagle optimization algorithm to enhance the algorithm's convergence speed and solution accuracy.

[0082] As an alternative approach, the improved Harris Eagle optimization algorithm includes the following enhancement strategies: initializing the population through chaotic mapping to increase the diversity of the initial population; replacing the linear decay in the standard algorithm with a nonlinear energy factor update formula to better balance global exploration and local exploitation; introducing a nonlinear jump strength strategy to enhance the algorithm's local optimization ability in the later stages of iteration; introducing adaptive inertia weights during position updates to improve the algorithm's convergence stability; and introducing adaptive mutation operations in the later stages of iteration to perturb and update the optimal individual, helping the algorithm escape local optima. Through these improvements, the algorithm can more efficiently find the global optimum under complex multi-constraint conditions.

[0083] S105. Based on the target scheduling scheme, perform scheduling control on each distributed power source, energy storage system and flexible load in the microgrid system.

[0084] Specifically, the target scheduling scheme includes the output plan of each micro-source at each time, the charging and discharging plan of the energy storage system, the power purchase and sale plan with the external power grid, and the dispatch plan of various flexible loads. By operating the microgrid system according to this scheme, the total operating cost of the system can be minimized.

[0085] It should be understood that, through the above steps, this embodiment organically couples the green certificate trading mechanism with the carbon trading mechanism, enabling the system to operate optimally under the combined effect of these two market-based tools. Simultaneously, by introducing flexible load scheduling compensation costs into the objective function and setting constraints on the adjustment capacity of flexible loads in the constraints, the effective utilization of load-side adjustment resources is achieved. The improved Harris Eagle optimization algorithm ensures efficiency and accuracy in solving problems under complex constraints. This achieves the goal of effectively reducing system operating costs, reducing carbon emissions, and improving the capacity for renewable energy absorption while ensuring the safe and stable operation of the system.

[0086] In some embodiments of the present invention, such as Figure 2 As shown, step S102, constructing the green certificate-carbon joint trading model, includes: S201, Quantify the carbon emission reductions generated by new energy power generation relative to the benchmark thermal power; S202. Use carbon emission reductions as the carbon emission credit limit for green certificates in the carbon trading market to establish a coupling and linkage relationship between green certificate trading and carbon trading.

[0087] Specifically, as an optional implementation method, the process of constructing the green certificate-carbon joint trading model in step S102 above can be implemented in the following way.

[0088] First, it is necessary to quantify the carbon emission reductions achieved by renewable energy generation relative to benchmark coal-fired power generation. Specifically, coal-fired power generation is typically chosen as the benchmark because it currently accounts for a large proportion of the electricity supply structure, and the carbon emission intensity of coal-fired power generation has a clear statistical basis. By comparing the carbon emission differences between renewable energy generation and coal-fired power generation per unit of electricity generated, the amount of carbon emissions avoided by renewable energy generation can be calculated.

[0089] For example, assuming wind power generation is 1000 kWh in a certain period, the carbon emission intensity per unit of electricity generated by coal-fired power generation is 889 g / kWh, while the carbon emission intensity of wind power generation is approximately zero, then the carbon emission reduction brought by wind power generation in that period would be 889 g / kWh multiplied by 1000 kWh, resulting in a carbon emission reduction of 889 kg. This carbon emission reduction reflects the actual contribution of new energy power generation to environmental protection.

[0090] Furthermore, the carbon emission reductions calculated above are used as the carbon emission credits for green certificates in the carbon trading market. In other words, each green electricity certificate obtained by the system not only represents the fulfillment of the corresponding renewable energy quota requirements, but also allows the system to offset an equivalent amount of carbon emission rights in the carbon trading market based on the carbon emission reductions corresponding to that certificate. This enables green certificates to participate in both the green certificate trading mechanism and the carbon trading mechanism, creating a linkage between these two market-based tools.

[0091] It should be noted that the correspondence between green certificates and carbon emission reductions can be set according to actual policies or market rules. For example, the carbon emission reduction represented by a unit of green certificate can be determined based on the difference in carbon emission intensity between coal-fired power generation and new energy power generation.

[0092] As a preferred approach, the formula for calculating carbon trading costs can be modified based on the aforementioned deduction mechanism.

[0093] Specifically, the carbon emission reductions corresponding to green certificates can be deducted from the system's actual carbon emissions, or the carbon emission reductions corresponding to green certificates can be included as additional carbon emission allowances in the system's tradable quota, thereby adjusting the system's net expenditures or net income in the carbon trading market.

[0094] It should be understood that the green certificate-carbon joint trading model established through the above method organically couples the originally independent green certificate trading mechanism with the carbon trading mechanism. On the one hand, green certificate trading provides a direct economic incentive for renewable energy power generation; on the other hand, the carbon emission reductions represented by green certificates can be converted into economic benefits in the carbon trading market, further amplifying the economic value of renewable energy power generation. This coupling relationship enables microgrid systems to obtain benefits from multiple dimensions simultaneously when participating in market-based trading, thereby more effectively guiding the system towards low-carbon and high-efficiency operation.

[0095] In some embodiments of the present invention, such as Figure 3 As shown, step S102, constructing the green certificate-carbon joint trading model, includes: S301. Determine the net revenue or net cost of green certificate trading based on the difference between the actual renewable energy generation of the microgrid and the preset quota requirements. S302. Determine the net revenue or net cost of carbon trading based on the difference between the actual carbon emissions of the microgrid system and the free carbon emission allowance. S303. Based on the carbon emission reduction generated by new energy power generation relative to the benchmark thermal power, the net revenue of green certificate trading is coupled into the calculation of carbon trading costs to update the net revenue or net cost of carbon trading.

[0096] Specifically, it is necessary to calculate the net revenue or net cost of green certificate trading and carbon trading separately, and then couple the two together.

[0097] First, the net revenue or net cost of green certificate trading is determined based on the difference between the actual renewable energy generation of the microgrid and the preset quota requirements. The preset quota requirements are usually set as the proportion of renewable energy generation to total load demand. For example, assuming the quota ratio is set at 20%, if the actual renewable energy generation of the microgrid exceeds the total load demand multiplied by 20% during a certain period, the green certificates corresponding to the excess can be sold on the market, generating revenue from green certificate trading. Conversely, if the actual generation is lower than the quota requirement, the shortfall needs to be covered by purchasing green certificates to meet the quota, resulting in green certificate trading costs.

[0098] Secondly, the net revenue or net cost of carbon trading is determined based on the difference between the actual carbon emissions of the microgrid system and the free carbon emission allowances. Free carbon emission allowances are allocated by relevant authorities based on the historical emission levels or industry benchmarks of each carbon-emitting entity. For example, for diesel generator sets, the free carbon emission allowance per unit of electricity can be set at 500 g / kWh; for electricity purchased from the upper-level grid, the free allowance can be set at 789 g / kWh. The sum of the actual emissions from diesel generator sets and the emissions from purchased coal-fired power is compared with the sum of the free allowances corresponding to each device. If the actual emissions are lower than the allowances, the surplus carbon emission rights can be sold in the carbon trading market, generating carbon trading revenue; if the actual emissions are higher than the allowances, carbon emission rights need to be purchased, incurring carbon trading costs.

[0099] Furthermore, based on the carbon emission reductions generated by renewable energy power generation relative to benchmark thermal power, the net revenue from the aforementioned green certificate trading is coupled into the calculation of carbon trading costs to update the net revenue or net cost of carbon trading. Specifically, when green certificate trading generates net revenue (i.e., the system sells green certificates due to exceeding renewable energy quotas), the renewable energy power generation represented by the green certificates corresponding to this revenue will inevitably generate corresponding carbon emission reductions. This portion of carbon emission reductions can be reflected in carbon trading assessments, for example, by deducting it from the system's actual carbon emissions, or by including it as additional carbon emission quotas in the system's tradable quota, thereby increasing the net revenue or decreasing the net cost of carbon trading. It should be noted that the coupling operation can be performed by first calculating the net results of green certificate trading and carbon trading separately, and then adding the net revenue from green certificates as an adjustment item to the carbon trading results to finally obtain the updated net revenue or net cost of carbon trading.

[0100] As an example, suppose that during a certain scheduling period, a microgrid obtains a net revenue of 385.1 yuan by selling green certificates, and simultaneously calculates the corresponding carbon emission reduction in kilograms for that period. In the carbon trading cost calculation, after including this carbon emission reduction as a deduction, the original carbon trading cost of 85.3 yuan is adjusted to a carbon trading revenue of 170.6 yuan. This demonstrates that through the aforementioned coupling mechanism, a positive interaction is formed between green certificate trading and carbon trading, allowing the system's renewable energy generation activities to generate economic returns in both markets.

[0101] It should be understood that the green certificate-carbon joint trading model established through the above method effectively integrates the originally relatively independent green certificate trading mechanism and the carbon trading mechanism. On the one hand, the system determines green certificate trading behavior based on the difference between renewable energy power generation and quota requirements; on the other hand, the system determines carbon trading behavior based on the difference between actual carbon emissions and free quotas. Based on this, the benefits or costs of the two types of trading are linked through carbon emission reductions, enabling the system's low-carbon energy supply behavior to receive positive incentives in both markets simultaneously, thereby more effectively guiding the microgrid system towards low-carbon and economical operation.

[0102] In some embodiments of the present invention, such as Figure 4 As shown, step S104 uses the improved Harris Eagle optimization algorithm to solve the objective function under constraints to output the target scheduling scheme, including: S401. Initialize the population using the Tent chaotic mapping to generate an initial candidate solution set that satisfies the constraints. S402. In the iterative optimization process, the objective function is used as the fitness evaluation benchmark. The nonlinear escape energy update formula and the nonlinear jump strength strategy are used to balance the global search and local exploitation. The initial candidate solution set is iteratively updated under the constraints. S403. In the later stage of the iteration, an adaptive Gaussian-Cauchy mutation operation is introduced to perturb and update the current best individual. The quality of the perturbed individual is evaluated based on the objective function. After the iteration termination condition is met, the target scheduling scheme that optimizes the objective function value is output from the final candidate solution set.

[0103] Specifically, chaotic mappings possess characteristics such as randomness, ergodicity, and orderliness, which can be used to increase population diversity and accelerate the convergence speed in the early stages of the algorithm. Therefore, this embodiment uses sequences generated by Tent chaos to initialize the population and improve the HHO algorithm, as shown in equation (46): (46) In the formula: For the generated chaotic sequence, i This represents the current iteration number.

[0104] In some embodiments of the present invention, the nonlinear escape energy update formula is shown in equation (47): (47) in, E The escape energy for the current iteration. The initial escape energy, The value is a random number between -1 and 1. t This represents the current iteration number. T This represents the maximum number of iterations. The nonlinear jump strength strategy is as follows: (48) in, J The jumping strength of the prey, t This represents the current iteration number. T The maximum number of iterations, r It is a random number between [0, 1].

[0105] It should be noted that in the HHO algorithm, the jump intensity of the prey is a random number between 0 and 2, which makes it difficult for the algorithm to have good convergence during the optimization process, resulting in low convergence accuracy. This embodiment adopts the nonlinear jump intensity strategy of Equation (48), which reduces the impact of jump intensity on energy decay in the later stage of iteration, thereby improving the convergence accuracy of the algorithm.

[0106] In some embodiments of the present invention, such as Figure 5 As shown, step S403 introduces an adaptive Gaussian-Cauchy mutation operation to perturb and update the optimal individual, including: S501. By using a weight coefficient that varies with the number of iterations, the Gaussian mutation operator and the Cauchy mutation operator are combined in a weighted manner to apply a perturbation to the current best individual and generate a new individual after mutation. S502. Use the greedy criterion to compare the fitness values ​​of individuals before and after mutation, and retain the individuals with better fitness for the next iteration.

[0107] Specifically, this mutation operation involves two different perturbation mechanisms: the Gaussian mutation operator and the Cauchy mutation operator. The Gaussian mutation operator generates perturbations based on a Gaussian distribution. Its characteristic is that it performs a fine-grained search within a local range, allowing for small adjustments to the current solution, which is beneficial for improving the accuracy of the solution, but its search range is relatively limited. The Cauchy mutation operator generates perturbations based on a Cauchy distribution. Its characteristic is that it has a wider tail distribution, capable of generating larger perturbations, which is beneficial for escaping the current region to explore a wider range, but there is a risk of skipping the optimal solution.

[0108] Furthermore, this embodiment introduces a weighting coefficient that varies with the number of iterations, weighting the two mutation operators mentioned above to apply a perturbation to the current optimal individual. This weighting coefficient is dynamically adjusted during the iteration process, allowing the mutation operation to adaptively switch its focus based on the algorithm's convergence state. For example, in the later stages of iteration, when the algorithm gradually converges to a certain region, the weight of the Gaussian mutation operator can be appropriately increased to enhance the local fine-grained search capability; at the same time, a certain proportion of the Cauchy mutation operator weight is retained to maintain a certain ability to escape local optima. The weighted mutation operator is then applied to the current optimal individual to generate a mutated new individual.

[0109] In a specific example, the adaptive inertia weights are shown in equations (49) and (50): (49) (50) In the formula: This is the initial inertia weight; The inertial weight is continuously updated and iterated to achieve the maximum number of iterations. This represents the current iteration number; Represents the highest iteration number. In the inertia weight... , This is more in line with the data expansion under this algorithm. As the state changes linearly, the value decreases from 0.9 to 0.4. In the early stage, the focus is more on enhancing exploration capabilities, while in the later stage, the focus is more on developing and expanding capabilities.

[0110] Furthermore, to prevent the HHO algorithm from getting stuck in local optima, this embodiment introduces a mutation perturbation operation to improve the diversity of the population. Cauchy mutation is known for its wide search range, but it carries the risk of skipping the optimal solution; while Gaussian mutation performs well in local searches, but its search range is relatively limited. Therefore, this embodiment proposes an adaptive Gaussian-Cauchy hybrid perturbation strategy, which combines the advantages of both and incorporates a greedy criterion. By comparing the fitness of the old and new positions, the position with higher fitness is selected to participate in the next round of iteration, thereby ensuring the search efficiency and effectiveness of the algorithm. As shown in Equation (51): (51) In the formula: This represents the position of the optimal individual after mutation in the t-th iteration; This represents the position of the optimal individual in the t-th iteration; For Gaussian mutation operator, The Cauchy mutation operator is used; the weight coefficients of the mutation operator change nonlinearly and gradually to ensure that the perturbation is uniform and stable in each iteration.

[0111] After performing the mutation perturbation, a greedy strategy is used to determine whether to update the position. Replacement is only performed if the fitness value of the new position is better than the original position, ensuring that each update selects only particles with better fitness. The greedy strategy selection is expressed as shown in equation (52): (52) in, This represents the position of the optimal individual after a greedy selection.

[0112] It should be noted that the evaluation criterion for the fitness value mentioned above is the objective function of the aforementioned optimized scheduling. The smaller the objective function value, the better the fitness.

[0113] It should be understood that through the aforementioned adaptive Gaussian-Cauchy mutation operation, the algorithm can maintain population diversity in the later stages of iteration, effectively reducing the probability of getting trapped in local optima. Simultaneously, due to the adoption of a greedy criterion, the mutation operation only accepts better individuals, ensuring that the population quality does not degrade due to random perturbations. This mechanism, while maintaining the algorithm's convergence, enhances its ability to escape local optima, thereby improving the accuracy of the final solution.

[0114] To verify the effectiveness of the improved Harris Eagle Optimization Algorithm (IHHO) in this embodiment, the standard HHO and three other optimization algorithms (PSO, GWO, and WOA) were selected for comparison. The population size for each algorithm was set to 30, and the number of iterations was 500. Each optimization algorithm was run independently 30 times on four benchmark functions. The optimal value of each optimization was recorded, and the average and standard deviation of the 30 optimal values ​​were calculated and compared with the theoretical optimal value of the benchmark functions. This comparison serves as the algorithm performance evaluation index. The optimal value primarily measures the accuracy of a single optimization, the average value measures the overall effect of multiple optimizations, and the standard deviation primarily measures the dispersion and stability of the error. The benchmark functions used for testing are shown in Table 1.

[0115] Table 1 Benchmark Function Table

[0116] The improved HHO algorithm in this embodiment, along with other algorithms, yielded the following optimization results for four benchmark functions: Figure 6 As shown. It should be noted that, to better study the merits of optimization, the convergence curve is recorded at each optimization iteration. The average of the 30 convergence curves is then calculated to obtain the average fitness curve, as shown below. Figure 6 The curves showing the change in the average fitness value of each optimization algorithm during the benchmark function optimization process are illustrated. Figure 6 In the table, (a) F1, (b) F2, (c) F3, and (d) F4 are the comparison results for the four benchmark test functions F1, F2, F3, and F4, respectively.

[0117] To verify the superiority of the improved Harris Eagle optimization algorithm in terms of iteration speed and accuracy, it was compared with the traditional Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization (WOA), and Harris Eagle (HHO) algorithms. The results of the five algorithms were compared. The curves are shown below. Figure 7 As shown, from Figure 7 It can be seen that all five algorithms can find the optimal scheduling scheme of the system. The improved Harris Eagle algorithm has a greater advantage over the other four algorithms, mainly reflected in faster convergence speed, higher solution accuracy, and the lowest total cost of final optimization. Around the 96th iteration, the improved Harris Eagle algorithm found the optimal value, while WOA, PSO, GWO, and HHO required approximately 170, 107, 127, and 144 iterations to converge, respectively. This is because the addition of the Tent chaotic mapping in the initialization phase resulted in a better population than the original random initialization. The improved inertial weights can better balance local optima and global optima, improving the convergence speed. Since the HHO algorithm has low convergence accuracy in local searches, this embodiment further enhances the algorithm's optimization ability in local searches by proposing a new prey energy decay model and a jumping strategy. In the later stages of iteration, a mutation perturbation operation is used to increase population diversity, making the particles random and enabling a faster search for the global optimum. As can be seen from the convergence curve, the improved algorithm converges faster in the early stages of iteration and can continuously find better solutions, verifying its superiority in terms of convergence speed and solution accuracy.

[0118] To further verify the effectiveness of the scheduling model proposed in this embodiment in promoting the consumption of new energy in microgrids, energy conservation and emission reduction, and cost reduction and efficiency improvement, five scenarios were set up for simulation comparison analysis. Scenario 1 is the traditional economic scheduling without considering carbon trading mechanisms, green certificate trading mechanisms, and flexible loads; Scenario 2 is the economic scheduling considering only carbon trading mechanisms, without considering green certificate trading mechanisms and flexible loads; Scenario 3 is the economic scheduling considering only green certificate trading mechanisms, without considering carbon trading mechanisms and flexible loads; Scenario 4 is the economic scheduling considering the joint trading of carbon trading mechanisms and green certificate trading mechanisms, but without considering flexible loads; Scenario 5 is the economic scheduling proposed in this embodiment that comprehensively considers the green certificate-carbon joint trading mechanism and the participation of flexible loads.

[0119] The dispatch results show that, after considering the joint trading mechanism of carbon trading and green certificates, the output of wind turbines and photovoltaics increased significantly, while the output of the grid and diesel engines decreased. Compared with scenarios 1 to 3, the overall system cost of scenario 4 is lower. This is mainly because after meeting the green certificate quota, the microgrid system can not only sell surplus green certificates, but also grant the system additional carbon emission rights during carbon emission rights assessment, achieving a virtuous cycle of the two mechanisms.

[0120] Further comparison of Scenario 4 and Scenario 5 reveals that Scenario 5 introduces flexible loads for dispatching, reducing the total system cost from RMB 2344.2 in Scenario 4 to RMB 2180.4, carbon emissions from 2167.1 kg to 1983.1 kg, and the renewable energy absorption rate from 83.4% to 83.5%. This demonstrates that the participation of flexible loads can effectively reduce system operating costs and carbon emissions, while further enhancing renewable energy absorption capacity.

[0121] Comparing the scheduling method proposed in this embodiment with the traditional economic scheduling model, the system's carbon emissions decreased from 3062.7 kg to 1983.1 kg, a reduction of approximately 35.2%; the total operating cost decreased from 2872.3 yuan to 2180.4 yuan, a reduction of 691.9 yuan; and the renewable energy absorption rate increased from 53.1% to 83.5%, an increase of 30.4 percentage points. These results confirm the practicality and effectiveness of the technical solution proposed in this embodiment in absorbing wind and solar energy, promoting deep emission reduction in the system, and improving economic efficiency.

[0122] To better implement the microgrid dispatching method considering green certificate-carbon co-operation in the embodiments of the present invention, based on the microgrid dispatching method considering green certificate-carbon co-operation, correspondingly, as follows: Figure 8 As shown, this embodiment of the invention also provides a microgrid dispatching device that considers green certificate-carbon co-operation. The microgrid dispatching device 800 that considers green certificate-carbon co-operation includes: The operating parameter acquisition module 801 is used to acquire the operating parameters of the microgrid system. The operating parameters include distributed power generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads. The linkage model construction module 802 is used to construct a green certificate-carbon joint trading model based on operating parameters. The green certificate-carbon joint trading model is used to establish the coupling and linkage relationship between green certificate trading and carbon trading. The constraint generation module 803 is used to construct an objective function for optimizing scheduling based on operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost, including the cost of green certificate-carbon joint trading and the cost of flexible load scheduling compensation. It also sets constraints including power balance constraints, output constraints of each micro-source, and the adjustment capability constraints of flexible loads. The scheduling scheme determination module 804 is used to solve the objective function under constraints using the improved Harris Eagle optimization algorithm to output the target scheduling scheme; The scheduling scheme execution module 805 is used to perform scheduling control on each distributed power source, energy storage system and flexible load in the microgrid system according to the target scheduling scheme.

[0123] The microgrid dispatching device 800 that takes into account green certificates and carbon linkage provided in the above embodiments can realize the technical solutions described in the above embodiments of the microgrid dispatching method that takes into account green certificates and carbon linkage. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the microgrid dispatching method that takes into account green certificates and carbon linkage, and will not be repeated here.

[0124] like Figure 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902, and a display 903. Figure 9 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0125] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the microgrid scheduling method that takes into account green certificates-carbon integration in this invention.

[0126] In some embodiments, processor 901 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 901 may be local or remote. In some embodiments, processor 901 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0127] In some embodiments, memory 902 may be an internal storage unit of electronic device 900, such as a hard disk or memory of electronic device 900. In other embodiments, memory 902 may also be an external storage device of electronic device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 900.

[0128] Furthermore, the memory 902 may include both internal storage units of the electronic device 900 and external storage devices. The memory 902 is used to store application software and various types of data installed on the electronic device 900.

[0129] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 903 is used to display information from electronic device 900 and to display a visual user interface. Components 901-903 of electronic device 900 communicate with each other via a system bus.

[0130] In one embodiment, when processor 901 executes the microgrid scheduling program that takes into account green certificate-carbon co-location in memory 902, the following steps can be implemented: The operating parameters of the microgrid system are obtained, including distributed generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads. Based on the operating parameters, a green certificate-carbon joint trading model is constructed. The green certificate-carbon joint trading model is used to establish the coupling and linkage relationship between green certificate trading and carbon trading. Based on operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost including green certificate-carbon joint trading costs and flexible load dispatch compensation costs, an objective function for optimized dispatch is constructed, and constraints including power balance constraints, output constraints of each micro-source, and adjustment capability constraints of flexible loads are set. An improved Harris Eagle optimization algorithm is used to find the optimal solution of the objective function under constraints, so as to output the target scheduling scheme; According to the target scheduling scheme, scheduling control is performed on each distributed power source, energy storage system and flexible load in the microgrid system.

[0131] It should be understood that when the processor 901 executes the microgrid scheduling program that takes into account the green certificate-carbon linkage in the memory 902, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0132] Furthermore, the embodiments of the present invention do not specifically limit the type of the electronic device 900 mentioned. The electronic device 900 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0133] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the microgrid dispatching methods that take into account green certificates and carbon integration provided in the above-described method embodiments.

[0134] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0135] The above provides a detailed description of the microgrid dispatching method, device, electronic equipment, and storage medium considering green certificate-carbon integration provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A microgrid dispatching method considering green certificate-carbon co-operation, characterized in that, include: The operating parameters of the microgrid system are obtained, including distributed generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads. Based on the aforementioned operating parameters, a green certificate-carbon joint trading model is constructed, which is used to establish the coupling and linkage relationship between green certificate trading and carbon trading. Based on the operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost including green certificate-carbon joint trading costs and flexible load scheduling compensation costs, an objective function for optimized scheduling is constructed, and constraints including power balance constraints, output constraints of each micro-source, and adjustment capability constraints of the flexible load are set. An improved Harris Eagle optimization algorithm is used to find the optimal solution for the objective function under the constraints, so as to output the target scheduling scheme; According to the target scheduling scheme, scheduling control is performed on each distributed power source, energy storage system and flexible load in the microgrid system.

2. The microgrid dispatching method considering green certificate-carbon co-operation according to claim 1, characterized in that, The flexible load includes at least one or more of the following: a load that can be reduced, a load that can be shifted, and a load that can be transferred. The flexible load scheduling compensation cost is calculated based on the scheduling volume of various types of flexible loads and the corresponding unit compensation price. The constraints on the adjustment capability of the flexible load include at least: the maximum number of reductions and continuous reduction time constraints of the load that can be reduced, the transferable time period constraints of the load that can be transferred, and the upper and lower limits of the transfer power and the total balance constraints before and after the transfer of the load that can be transferred.

3. The microgrid dispatching method considering green certificate-carbon co-operation according to claim 1, characterized in that, The construction of the green certificate-carbon joint trading model includes: Quantify the carbon emission reductions generated by new energy power generation relative to the benchmark thermal power generation; The carbon emission reductions will be used as the carbon emission credit limit for green certificates in the carbon trading market, thus establishing a coupling and linkage between green certificate trading and carbon trading.

4. The microgrid dispatching method considering green certificate-carbon co-operation according to claim 1, characterized in that, The construction of the green certificate-carbon joint trading model includes: The net revenue or net cost of green certificate trading is determined based on the difference between the actual renewable energy generation of the microgrid and the preset quota requirements. The net revenue or net cost of carbon trading is determined based on the difference between the actual carbon emissions of the microgrid system and the free carbon emission allowances. Based on the carbon emission reductions generated by new energy power generation relative to the benchmark thermal power generation, the net revenue from the green certificate trading is coupled into the calculation of carbon trading costs to update the net revenue or net cost of the carbon trading.

5. The microgrid dispatching method considering green certificate-carbon co-operation according to claim 1, characterized in that, The improved Harris Eagle optimization algorithm is used to solve the objective function under the constraints to find the optimal solution and output the target scheduling scheme, including: The population is initialized using the Tent chaotic mapping to generate an initial candidate solution set that satisfies the constraints. During the iterative optimization process, the objective function is used as the fitness evaluation benchmark. The nonlinear escape energy update formula and the nonlinear jump strength strategy are used to balance global search and local exploitation. The initial candidate solution set is iteratively updated under the constraints. In the later stages of the iteration, an adaptive Gaussian-Cauchy mutation operation is introduced to perturb and update the current best individual. The quality of the perturbed individual is evaluated based on the objective function. After the iteration termination condition is met, the target scheduling scheme that optimizes the objective function value is output from the final candidate solution set.

6. The microgrid dispatching method considering green certificate-carbon co-operation according to claim 5, characterized in that, The introduction of adaptive Gaussian-Cauchy mutation operation to perturb and update the optimal individual includes: By using a weight coefficient that varies with the number of iterations, the Gaussian mutation operator and the Cauchy mutation operator are combined in a weighted manner to apply a perturbation to the current best individual and generate a new individual after mutation. The greedy criterion is used to compare the fitness values ​​of individuals before and after mutation, and individuals with better fitness are retained for the next iteration.

7. The microgrid dispatching method considering green certificate-carbon co-operation according to claim 5, characterized in that, The nonlinear escape energy update formula is as follows: in, E The escape energy for the current iteration. The initial escape energy, t This represents the current iteration number. T This represents the maximum number of iterations. The nonlinear jump strength strategy is as follows: in, J The jumping strength of the prey, t This represents the current iteration number. T The maximum number of iterations, r It is a random number between [0, 1].

8. A microgrid dispatching device considering green certificate-carbon co-operation, characterized in that, include: The operating parameter acquisition module is used to acquire the operating parameters of the microgrid system. The operating parameters include distributed power generation parameters, load forecast data, green certificate trading market parameters, carbon trading market parameters, and time-of-use electricity price parameters. The load forecast data includes the response characteristics of flexible loads. The linkage model construction module is used to construct a green certificate-carbon joint trading model based on the operating parameters. The green certificate-carbon joint trading model is used to establish a coupling and linkage relationship between green certificate trading and carbon trading. The constraint generation module is used to construct an objective function for optimizing scheduling based on the operating parameters and the green certificate-carbon joint trading model, with the goal of minimizing the total system operating cost, including the cost of green certificate-carbon joint trading and the cost of flexible load scheduling compensation. The module also sets constraints including power balance constraints, output constraints of each micro-source, and the adjustment capability constraints of the flexible load. The scheduling scheme determination module is used to solve the objective function under the constraints using an improved Harris Eagle optimization algorithm to output the target scheduling scheme. The scheduling scheme execution module is used to perform scheduling control on each distributed power source, energy storage system and flexible load in the microgrid system according to the target scheduling scheme.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the microgrid dispatching method considering green certificate-carbon linkage as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the microgrid dispatching method considering green certificate-carbon integration as described in any one of claims 1 to 7.

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