Cooperative scheduling method based on parrot optimization algorithm and peak-valley time-of-use electricity price

Abstract

The invention discloses a parrot-shaped north pole optimization algorithm and peak-valley time-of-use electricity price-based collaborative scheduling method, and belongs to the technical field of micro-grid intelligent scheduling and renewable energy management, and the method comprises the following steps: S1, building a micro-grid system model; s2, operation constraint conditions are constructed, wherein the operation constraint conditions comprise power balance which needs to be met by a fan, a photovoltaic system, a gas turbine, a fuel cell, a storage battery and power exchange power with the power grid in the micro-grid, upper and lower limit constraints of power of each component of the micro-grid and state constraints of the storage battery; s3, constructing a target optimization function of the operation cost and pollutant discharge; and S4, applying the target optimization function, and outputting an optimal scheduling scheme. By the adoption of the collaborative scheduling method based on the parrot optimization algorithm and the peak-valley time-of-use electricity price, the optimization precision and convergence efficiency of the system can be remarkably improved, meanwhile, high reliability and robustness of the scheduling process are ensured, and finally intelligent collaborative scheduling of micro-grid energy is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent dispatching and renewable energy management technology for microgrids, and in particular to a collaborative dispatching method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing. Background Technology

[0002] In existing technologies, the large-scale application of renewable energy has altered the operational structure of power systems. The intermittent output of wind and solar power exacerbates grid frequency fluctuations and reduces voltage stability margins, making traditional centralized dispatching models unsuitable for scenarios with a high proportion of renewable energy. The spatiotemporal randomness of wind and solar resources and the widening peak-valley load difference intensify the source-load balance contradiction. Under extreme weather conditions, a sudden drop in wind and solar output combined with peak loads can easily trigger voltage over-limit risks, necessitating the sacrifice of economic efficiency to maintain system safety.

[0003] Microgrids mitigate wind and solar power fluctuations and improve power supply reliability through a source-grid-load-storage collaborative architecture, but they are constrained by three bottlenecks: the uncertainty of renewable energy leads to inaccurate dispatching schemes; the dynamic characteristics of loads cause response lags; and the heterogeneity of diverse equipment creates barriers to collaborative optimization. Traditional dispatching methods struggle to balance economy and environmental protection, and lack robustness and real-time performance.

[0004] Current research on integrating swarm intelligence algorithms with dynamic electricity pricing strategies has significant shortcomings: the fixed parameter mechanism of swarm intelligence algorithms cannot adapt to disturbances on both the source and load sides, and is prone to getting trapped in local extrema; the peak-valley time division of dynamic electricity pricing is rigid, and its mapping with user response lags behind. The core problem lies in the disconnect between the algorithm layer and the market layer, and the lack of two-way feedback between supply-side scheduling and demand-side guidance, which prevents the coordinated improvement of clean energy consumption, system economics, and environmental performance, making it difficult to meet the needs of intelligent dispatching of microgrids. Summary of the Invention

[0005] The purpose of this invention is to provide a collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing, which can significantly improve the optimization accuracy and convergence efficiency of the system, while ensuring the high reliability and robustness of the scheduling process, and ultimately realize the intelligent collaborative scheduling of microgrid energy.

[0006] To achieve the above objectives, this invention provides a collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing, comprising the following steps: Step S1: Establish a microgrid system model, describe the nonlinear relationship between wind turbine power and wind speed through piecewise functions, and construct a mapping model between photovoltaic power and environmental parameters based on the photovoltaic effect; Step S2: Construct operational constraints, including the power balance that the wind turbines, photovoltaics, gas turbines, fuel cells, batteries, and power exchange with the grid in the microgrid need to meet, as well as the upper and lower limits of the power of each component of the microgrid and the state constraints of the batteries; Step S3: Construct the objective optimization function for operating costs and pollutant emissions; Step S4: Apply the objective optimization function to output the optimal scheduling scheme.

[0007] Preferably, in step S1, the fan power is specifically: ; in, Indicates the wind turbine unit at the current wind speed The actual output power at the specified level; This indicates the rated power of the fan; This indicates the cut-in wind speed when the extension unit is working normally; This indicates the rated wind speed of the fan; This indicates the maximum wind speed at which the fan operates normally; The specific photovoltaic power is as follows: ; in, This indicates the actual power generation of the solar photovoltaic panel; Indicates the rated power of the solar photovoltaic panel; Indicates the current ambient light intensity; This represents the light intensity under ideal conditions, 1 kW / m². 2 ; Indicates the power temperature coefficient; This indicates the surface temperature of the solar photovoltaic panel during operation. This represents the surface temperature of a solar photovoltaic panel under ideal conditions. t This indicates a time period index.

[0008] Preferably, in step S2, the power balancing specifically involves: ; in, Indicates the gas turbine in the first t Output power over a time period; Indicates that the fuel cell is in the first t Output power over a time period; Indicates the battery at the first t The charging and discharging power over a time period, with a positive value indicating discharging and a negative value indicating charging; This indicates that the microgrid and the main grid are in the first... t The exchange power over a time period, a positive value indicates that electricity is purchased from the grid, and a negative value indicates that electricity is sold to the grid; Indicates the first t Total load demand power of the microgrid during the time period; The power operating constraints that wind turbines, photovoltaic systems, gas turbines, fuel cells, batteries, and power exchanged with the grid in a microgrid must meet are as follows: ; in, and These are the lower and upper limits of the wind turbine's electrical power, respectively. and These are the lower and upper limits of photovoltaic power output, respectively. and These are the lower and upper limits of the electrical power of the gas turbine, respectively. and These represent the lower and upper limits of the fuel cell's electrical power, respectively. and These are the lower and upper limits of the battery's charging and discharging power, respectively. and These represent the lower and upper limits of the electrical power exchanged with the power grid, respectively.

[0009] Preferably, in step S2, the battery state is the ratio of the remaining battery charge to the maximum capacity, denoted as SOC. Its initial value, update formula, upper and lower limit constraints, and initial and final equality constraints are as follows: ; in, The initial state value of the battery is set to 0.5; Bat is the maximum capacity of the battery, which is 1MW in this invention. and These are the lower and upper limits of the battery's electrical state, used to prevent damage to the battery itself caused by overcharging and over-discharging. and The values ​​are 0.3 and 0.95 respectively.

[0010] Preferably, in step S3, the total operating cost of the microgrid is... F 1 represents the sum of fuel costs, start-up and shutdown costs of each component, and operation and maintenance costs, specifically: ; in, These represent the power exchange capabilities of photovoltaics, wind turbines, gas turbines, fuel cells, batteries, and the grid. t The total operating cost per hour is calculated using the following formulas: ; in, These represent the operation and maintenance costs, fuel costs, and start-up and shutdown costs of the gas turbine, respectively. These represent the operating cost, fuel cost, and start-up / shutdown cost of the fuel cell, respectively. These represent the unit operation and maintenance cost coefficients for photovoltaics, wind turbines, gas turbines, fuel cells, and batteries, respectively. The term "time-of-use electricity price" represents the electricity price adopted after peak-valley division; C represents the unit price of gas for the micro gas turbine, which is 3 yuan / m³ in this invention. 3 ; This indicates the length of a unit scheduling time period, which is 1 hour in this invention; LHV is the calorific value of natural gas. The power generation efficiency of gas turbines and fuel cells are respectively. These respectively represent the gas turbine and fuel cell in t Start-up and shutdown costs per hour; These respectively represent the gas turbine in t Hours and t -1 hour of operation status These respectively represent the fuel cells in t Hours and t The running status after -1 hour is represented by a 0-1 variable, where 0 indicates shutdown and 1 indicates operation.

[0011] Preferably, in step S3, the total amount of pollution without emissions... F 2 Specifically: ; in, These represent the operating conditions of the gas turbine. CO 2. SO 2 and NO X The unit's emission system; These represent the operating conditions of the gas turbine. CO 2. SO 2 and NO X The unit's emission system; , , These represent the battery's operating conditions. CO 2. SO 2 and NO X The unit's emission system.

[0012] Preferably, step S4 specifically includes: Step S401: Input the power load data sequence; Step S402: Sort the power load data sequence in ascending order; Step S403: Initialize the boundary decision vector and ,in, Used to divide the valley period and the plain period, Used to distinguish between off-peak and peak periods; Step S404: Define the objective function to obtain the minimum mean square distance. Update the boundary decision vector and The peak-flat-valley cycle of the output load.

[0013] Preferably, step S401 specifically includes: Input power load data, defined as a sequence : ; in, Indicates the first K Hourly power load, K For indexing; The new sequence is formed after permutation in step S402. : ; in, Indicates the new sequence number K Hourly power load.

[0014] Preferably, in step S403, the boundary decision vector and Specifically: ; In step S404, the mean square distance of the load sequence is... Defined as the objective function, by... and From respectively Move to and Move to ,generate F The minimum mean square distance is determined by iteratively solving multiple values. and their corresponding boundary variables and .

[0015] Preferably, within the peak-flat-valley cycle of the load, the interval The load in the interval is at its lowest point. The load in the interval is during normal periods. The load is at its peak, specifically: ; in, m Indicates peak hours, normal hours, or off-peak hours; These represent time period indices belonging to peak hours, normal hours, and valley hours, respectively. i This represents a time period index, with a range of [1, 24]. Represents the first of the load sequence m Cluster centers for each cycle; Indicates the first in the load sequence Load power values ​​for a given time period; Indicates belonging to The total number of time periods.

[0016] Therefore, this invention adopts the above-mentioned collaborative scheduling method based on the Arctic Puffin Optimization Algorithm (APO) and peak-valley time-of-use pricing. This method deeply integrates intelligent optimization algorithms and dynamic pricing mechanisms. Through the adaptive global search strategy of the APO algorithm, it significantly improves the optimization accuracy and convergence efficiency of the system, while ensuring the high reliability and robustness of the scheduling process, and ultimately realizes intelligent collaborative scheduling of microgrid energy.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] Figure 1 This is a flowchart of an embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of the present invention; Figure 2 This is a microgrid system framework diagram based on the Arctic Puffin optimization algorithm and the collaborative scheduling method of peak-valley time-of-use pricing, as described in this invention. Figure 3 This is a flowchart of step S4 in the embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of the present invention. Figure 4 This is a result diagram output from an embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of the present invention; Figure 5 This is a diagram of the photovoltaic power output of wind turbines in an embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of this invention. Figure 6 This is a load curve before and after peak-valley time-of-use pricing optimization in an embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of this invention; Figure 7 This is an iterative curve of the fitness value in the APO algorithm in the embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of this invention; Figure 8 This is a microgrid operation optimization scheduling diagram in an embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of the present invention; Figure 9 This is a dynamic trajectory change curve of battery SOC (State of Charge) in an embodiment of the collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing of this invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," or similar terms as used in this invention, mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0021] Example 1 like Figure 1 As shown, this invention provides a collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing, including the following steps: Step S1: Establish a microgrid system model. Figure 2 This paper describes the architecture of a distributed renewable energy microgrid system, including a power grid, photovoltaic (PV) systems, wind turbines, fuel cells, batteries, gas turbines, and electrical loads. The power outputs of wind turbines, PV systems, fuel cells, and gas turbines power the entire system. The system mitigates wind and solar power fluctuations through a multi-energy complementary architecture, with batteries serving as the core buffer unit. When there is an imbalance between renewable energy output and load demand, batteries prioritize charging and discharging to regulate power balance. If the regulation margin is exhausted, electricity is purchased and sold through the grid interface. Finally, by employing a population-based collaborative search strategy using the Arctic Puffin optimization algorithm and dynamic boundary decision-making based on peak-valley pricing, the system significantly improves renewable energy absorption capacity and overall energy efficiency while ensuring high robustness of the dispatch scheme.

[0022] The nonlinear relationship between wind turbine power and wind speed is described using piecewise functions, and a mapping model between photovoltaic power and environmental parameters is constructed based on the photovoltaic effect; the specific wind turbine power is: ; in, Indicates the wind turbine unit at the current wind speed The actual output power at the specified level; This indicates the rated power of the fan; This indicates the cut-in wind speed when the extension unit is working normally; This indicates the rated wind speed of the fan; This indicates the maximum wind speed at which the fan operates normally; The specific photovoltaic power is as follows: ; in, This indicates the actual power generation of the solar photovoltaic panel; Indicates the rated power of the solar photovoltaic panel; Indicates the current ambient light intensity; This represents the light intensity under ideal conditions, 1 kW / m². 2 ; Indicates the power temperature coefficient; This indicates the surface temperature of the solar photovoltaic panel during operation. This represents the surface temperature of a solar photovoltaic panel under ideal conditions. t This indicates a time period index.

[0023] Step S2: Construct operational constraints, including the power balance that wind turbines, photovoltaics, gas turbines, fuel cells, batteries, and power exchange with the grid in the microgrid need to meet, as well as the upper and lower limits of power for each component of the microgrid and the state constraints of the batteries.

[0024] Power balance specifically refers to: ; in, Indicates the gas turbine in the first t Output power over a time period; Indicates that the fuel cell is in the first t Output power over a time period; Indicates the battery at the first t The charging and discharging power over a time period, with a positive value indicating discharging and a negative value indicating charging; This indicates that the microgrid and the main grid are in the first... t The exchange power over a time period, a positive value indicates that electricity is purchased from the grid, and a negative value indicates that electricity is sold to the grid; Indicates the first t Total load demand power of the microgrid during the time period; The power operating constraints that wind turbines, photovoltaic systems, gas turbines, fuel cells, batteries, and power exchanged with the grid in a microgrid must meet are as follows: ; in, and These are the lower and upper limits of the wind turbine's electrical power, respectively. and These are the lower and upper limits of photovoltaic power output, respectively. and These are the lower and upper limits of the electrical power of the gas turbine, respectively. and These represent the lower and upper limits of the fuel cell's electrical power, respectively. and These are the lower and upper limits of the battery's charging and discharging power, respectively. and These represent the lower and upper limits of the electrical power exchanged with the power grid, respectively.

[0025] The battery state of charge (SOC) is the ratio of the battery's remaining charge to its maximum capacity. Its initial value, update formula, upper and lower limit constraints, and initial-final equality constraint are as follows: ; in, The initial state value of the battery is set to 0.5; Bat is the maximum capacity of the battery, which is 1MW in this invention. and These are the lower and upper limits of the battery's electrical state, used to prevent damage to the battery itself caused by overcharging and over-discharging. and The values ​​are 0.3 and 0.95 respectively.

[0026] Step S3: Construct the objective optimization function for operating costs and pollutant emissions; This invention considers two objectives: operating cost and pollutant emissions. It uses different weighting coefficients to balance the relationship between operating cost and pollutant emissions, ultimately taking the minimum value of the proportion as the required fitness condition for individual Arctic puffins. Specifically, it can be expressed as: ; in, and The adaptive weighting coefficients for microgrid operating costs and pollutant emissions are respectively calculated as follows: ; in, and for Initial value and final value, and for Initial and final values, g is the current iteration number, and G is the maximum iteration number.

[0027] Total operating cost of microgrids F 1 represents the sum of fuel costs, start-up and shutdown costs of each component, and operation and maintenance costs, specifically: ; in, These represent the power exchange capabilities of photovoltaics, wind turbines, gas turbines, fuel cells, batteries, and the grid. t The total operating cost per hour is calculated using the following formulas: ; in, These represent the operation and maintenance costs, fuel costs, and start-up and shutdown costs of the gas turbine, respectively. These represent the operating cost, fuel cost, and start-up / shutdown cost of the fuel cell, respectively. These represent the unit operation and maintenance cost coefficients for photovoltaics, wind turbines, gas turbines, fuel cells, and batteries, respectively. The term "time-of-use electricity price" represents the electricity price adopted after peak-valley division; C represents the unit price of gas for the micro gas turbine, which is 3 yuan / m³ in this invention. 3 ; This indicates the length of a unit scheduling time period, which is 1 hour in this invention; LHV is the calorific value of natural gas. The power generation efficiency of gas turbines and fuel cells are respectively. These respectively represent the gas turbine and fuel cell in t Start-up and shutdown costs per hour; These respectively represent the gas turbine in t Hours and t -1 hour of operation status These respectively represent the fuel cells in t Hours and t The running status after -1 hour is represented by a 0-1 variable, where 0 indicates shutdown and 1 indicates operation.

[0028] Total amount of pollution without emissions F 2 Specifically: ; in, These represent the operating conditions of the gas turbine. CO 2. SO 2 and NO X The unit's emission system; These represent the operating conditions of the gas turbine. CO 2. SO 2 and NO X The unit's emission system; , , These represent the battery's operating conditions. CO 2. SO 2 and NO X The unit's emission system.

[0029] Step S4: Apply the objective optimization function to output the optimal scheduling scheme.

[0030] Step S401: Input power load data sequence; Input power load data, defined as a sequence. : ; in, Indicates the first K Hourly power load, K For indexing; Step S402: Sort the power load data sequence in ascending order; a new sequence is formed after sorting. : ; in, Indicates the new sequence number K Hourly power load.

[0031] Step S403: Initialize the boundary decision vector and ,in, Used to divide the valley period and the plain period, Used to distinguish between off-peak and peak periods; boundary decision vector and Specifically: ; Step S404: Calculate the mean square distance of the load sequence. Defined as the objective function, by... and From respectively Move to and Move to ,generate F The minimum mean square distance is determined by iteratively solving multiple values. and their corresponding boundary variables and Update the boundary decision vector and The peak-flat-valley cycle of the output load.

[0032] During the peak-flat-valley cycle of the load, the interval The load in the interval is at its lowest point. The load in the interval is during normal periods. The load is at its peak, specifically: ; in, m Indicates peak hours, normal hours, or off-peak hours; These represent time period indices belonging to peak hours, normal hours, and valley hours, respectively. i This represents a time period index, with a range of [1, 24]. Represents the first of the load sequence m Cluster centers for each cycle; Indicates the first in the load sequence Load power values ​​for a given time period; Indicates belonging to The total number of time periods.

[0033] through Figure 3 After the algorithm iterates as shown, it outputs the number of time periods for peak, normal, and valley periods, along with their respective indices. See details for the results. Figure 4 Electricity prices are set based on peak and off-peak hours.

[0034] This method verifies the robustness of the algorithm under typical solar power output scenarios: such as Figure 5 As shown, wind turbine output exhibits a reverse peak-shaving characteristic, being high at night and low during the day, while photovoltaic output forms a single-peak curve at midday. The superposition of the two results in a sustained power surplus during the day, but wind and solar output decrease simultaneously in the evening, creating a sharp supply-demand imbalance with the evening peak load. This typical anti-phase fluctuation characteristic accurately simulates the spatiotemporal mismatch problem of source and load in scenarios with a high proportion of renewable energy integration, providing an empirical basis for verifying the adaptability of the APO algorithm under power balance constraints.

[0035] Peak-valley pricing strategies based on moving boundary technology effectively guide load curve optimization. For example... Figure 6 As shown, after the implementation of time-of-use pricing, the original evening peak load (19:00) was significantly reduced through demand response, with transferable load migrating to the midday period when photovoltaic output is abundant. Notably, during the period from 18:00 to 20:00 when photovoltaic output is low, demand response, in conjunction with peak shaving by thermal power units and battery discharge, kept net purchased power within a safe threshold. This mechanism, by precisely matching electricity price incentives with real-time source and load status, effectively narrows the load peak-valley difference, demonstrating its synergistic regulatory value in smoothing system fluctuations and reducing operating costs.

[0036] The APO algorithm demonstrates excellent convergence performance in scenarios with strong wind and light fluctuations. For example... Figure 7 As shown, the algorithm achieves dynamic strategy switching through a behavior conversion factor B: rapidly reducing the fitness value in the early stage of iteration, entering a fine-tuning optimization phase in the middle stage, and finally converging to the global optimum within a reasonable number of iterations. Compared with traditional algorithms, APO significantly improves convergence speed, effectively alleviating the problem of excessive computational burden in high-dimensional optimization and meeting the real-time requirements of microgrid scheduling.

[0037] Empirical evidence of optimal scheduling schemes is as follows: Figure 8 As shown, the battery strictly adheres to the "valley charging, peak discharging" strategy, charging during off-peak hours to absorb surplus wind and solar power, and discharging during peak hours to participate in peak shaving; the fuel cell precisely engages during peak load periods, effectively compensating for the power output gap caused by wind and solar power. This scheme significantly reduces the peak-valley load difference, power voltage drop during periods sensitive to purchased electricity costs, and reduces the frequency of equipment start-ups and shutdowns. Empirical verification demonstrates the technical advantages of the APO algorithm in multi-objective collaborative scheduling, maintaining a high level of renewable energy absorption.

[0038] The battery's SOC dynamic trajectory strictly adheres to safety constraints, such as... Figure 9As shown, the initial SOC was at a reasonable level and remained within the safe range throughout the day's charge-discharge cycles. During periods of sudden load changes, the algorithm mechanism adjusted the charge-discharge rate in real time, successfully avoiding the risk of exceeding limits. The charge-discharge process was precisely matched with periods of excess wind and solar power output, verifying the effectiveness of the "priority consumption of new energy" principle and demonstrating that this method maximizes the utilization of energy storage regulation potential while ensuring the safe operation of the equipment.

[0039] Therefore, the present invention adopts the above-mentioned collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing, which can significantly improve the optimization accuracy and convergence efficiency of the system, while ensuring the high reliability and robustness of the scheduling process, and ultimately realize the intelligent collaborative scheduling of microgrid energy.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing, characterized in that, Includes the following steps: Step S1: Establish a microgrid system model, describe the nonlinear relationship between wind turbine power and wind speed through piecewise functions, and construct a mapping model between photovoltaic power and environmental parameters based on the photovoltaic effect; Step S2: Construct operational constraints, including the power balance that the wind turbines, photovoltaics, gas turbines, fuel cells, batteries, and power exchange with the grid in the microgrid need to meet, as well as the upper and lower limits of the power of each component of the microgrid and the state constraints of the batteries; Step S3: Construct the objective optimization function for operating costs and pollutant emissions; Step S4: Apply the objective optimization function to output the optimal scheduling scheme.

2. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 1, characterized in that, In step S1, the specific power of the fan is as follows: ； in, Indicates the wind turbine unit at the current wind speed The actual output power at the specified level; This indicates the rated power of the fan; This indicates the cut-in wind speed when the fan is operating normally; This indicates the rated wind speed of the fan; This indicates the cut-out velocity of the fan; The specific photovoltaic power is as follows: ； in, This indicates the actual power generation of the solar photovoltaic panel; Indicates the rated power of the solar photovoltaic panel; Indicates the current ambient light intensity; This represents the light intensity under ideal conditions, 1 kW / m². 2 ; Indicates the power temperature coefficient; This indicates the surface temperature of the solar photovoltaic panel during operation. This represents the surface temperature of a solar photovoltaic panel under ideal conditions. t This indicates a time period index.

3. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 2, characterized in that, In step S2, the power balancing specifically involves: ； in, Indicates the gas turbine in the first t Output power over a time period; Indicates that the fuel cell is in the first t Output power over a time period; Indicates the battery at the first t The charging and discharging power over a time period, with a positive value indicating discharging and a negative value indicating charging; This indicates that the microgrid and the main grid are in the first... t The exchange power over a time period, a positive value indicates that electricity is purchased from the grid, and a negative value indicates that electricity is sold to the grid; Indicates the first t Total load demand power of the microgrid during the time period; The power operating constraints that wind turbines, photovoltaic systems, gas turbines, fuel cells, batteries, and power exchanged with the grid in a microgrid must meet are as follows: ； in, and These are the lower and upper limits of the wind turbine's electrical power, respectively. and These are the lower and upper limits of photovoltaic power output, respectively. and These are the lower and upper limits of the electrical power of the gas turbine, respectively. and These represent the lower and upper limits of the fuel cell's electrical power, respectively. and These are the lower and upper limits of the battery's charging and discharging power, respectively. and These represent the lower and upper limits of the electrical power exchanged with the power grid, respectively.

4. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 3, characterized in that, In step S2, the battery state is the ratio of the remaining battery charge to the maximum capacity, denoted as SOC. Its initial value, update formula, upper and lower limit constraints, and initial and final equality constraints are as follows: ； in, The initial state of the battery is represented by the value of Bat; the maximum capacity of the battery is represented by Bat. and These represent the lower and upper limits of the battery's state of charge.

5. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 4, characterized in that, In step S3, the total operating cost of the microgrid F 1 represents the sum of fuel costs, start-up and shutdown costs of each component, and operation and maintenance costs, specifically: ； in, These represent the power exchange capabilities of photovoltaics, wind turbines, gas turbines, fuel cells, batteries, and the grid. t The total operating cost per hour is calculated using the following formulas: ； in, These represent the operation and maintenance costs, fuel costs, and start-up and shutdown costs of the gas turbine, respectively. These represent the operating cost, fuel cost, and start-up / shutdown cost of the fuel cell, respectively. These represent the unit operation and maintenance cost coefficients for photovoltaics, wind turbines, gas turbines, fuel cells, and batteries, respectively. C represents the time-of-use electricity price adopted after peak-valley division; C is the unit price of gas for micro gas turbines. Indicates the length of a unit scheduling time period; LHV is the calorific value of natural gas; The power generation efficiency of gas turbines and fuel cells are respectively. These respectively represent the gas turbine and fuel cell in t Start-up and shutdown costs per hour; These respectively represent the gas turbine in t Hours and t -1 hour of operation status These respectively represent the fuel cells in t Hours and t The running status after -1 hour is represented by a 0-1 variable, where 0 indicates shutdown and 1 indicates operation.

6. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 5, characterized in that, In step S3, the total amount of pollution without emissions. F 2 Specifically: ； in, These represent the operating conditions of the gas turbine. CO 2. SO 2 and NO X The unit's emission system; These represent the operating conditions of the gas turbine. CO 2. SO 2 and NO X The unit's emission system; , , These represent the battery's operating conditions. CO 2. SO 2 and NO X The unit's emission system.

7. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 6, characterized in that, Step S4 is as follows: Step S401: Input the power load data sequence; Step S402: Sort the power load data sequence in ascending order; Step S403: Initialize the boundary decision vector and ,in, Used to divide the valley period and the plain period, Used to distinguish between off-peak and peak periods; Step S404: Define the objective function to obtain the minimum mean square distance. Update the boundary decision vector and The peak-flat-valley cycle of the output load.

8. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 7, characterized in that, Step S401 is as follows: Input power load data, defined as a sequence : ； in, Indicates the first K Hourly power load, K For indexing; The new sequence is formed after permutation in step S402. : ； in, Indicates the new sequence number K Hourly power load.

9. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 8, characterized in that, In step S403, the boundary decision vector and Specifically: ； In step S404, the mean square distance of the load sequence is... Defined as the objective function, by... and From respectively Move to and Move to ,generate F The minimum mean square distance is determined by iteratively solving multiple values. and their corresponding boundary variables and .

10. The collaborative scheduling method based on the Arctic Puffin optimization algorithm and peak-valley time-of-use pricing as described in claim 9, characterized in that, During the peak-flat-valley cycle of the load, the interval The load in the interval is at its lowest point. The load in the interval is during normal periods. The load is at its peak, specifically: ； in, m Indicates peak hours, normal hours, or off-peak hours; These represent time period indices belonging to peak hours, normal hours, and valley hours, respectively. i This represents a time period index, with a range of [1, 24]. Represents the first of the load sequence m Cluster centers for each cycle; Indicates the first in the load sequence Load power values ​​for a given time period; Indicates belonging to The total number of time periods.

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