Park flexible resource regulation method, device, system and medium

Abstract

The application relates to the technical field of flexible resource regulation, and provides a park flexible resource regulation method, device, system and medium. An implementation scheme is as follows: based on various flexible resource data, a multi-objective static optimization model is constructed with the minimum power grid power purchase cost and carbon emission, the maximum renewable energy consumption rate and power supply reliability as targets; based on a first constraint condition of the park, a multi-objective static optimization model is solved through a non-dominated sorting genetic algorithm to obtain a Pareto optimal solution set; a multi-objective dynamic optimization model is constructed with the maximum carbon emission benefit and the minimum power consumption cost of the park as targets, wherein a dynamic priority coefficient in the multi-objective dynamic optimization model is regulated by a candidate regulation solution in the Pareto optimal solution set; based on a second constraint condition of the park, the multi-objective dynamic optimization model is solved to obtain a flexible resource regulation scheme. The embodiments of the application can realize resource collaborative optimization and adaptive operation under the economic and low-carbon targets.

Description

Technical Field

[0001] This invention relates to the field of flexible resource regulation technology, and in particular to a method, device, system and medium for regulating flexible resources in a park. Background Technology

[0002] Against the backdrop of the construction of new power systems, flexible resources, represented by distributed photovoltaics, energy storage, and adjustable loads, have been widely deployed on the user side, such as in industrial parks and commercial buildings.

[0003] Existing energy management and control methods in industrial parks often focus solely on reducing electricity purchase costs or mitigating load fluctuations, or only optimize certain equipment independently under fixed operating strategies. This approach fails to achieve an effective balance between economic efficiency and low carbon emissions.

[0004] In addition, some resource scheduling methods that introduce multi-objective optimization ideas are usually based on static load or empirical parameters for unified modeling, but fail to fully characterize the differences between different flexible resources, resulting in insufficient resource coordination and poor adaptability of optimization results to changes in the operating environment.

[0005] Therefore, in actual operation, when photovoltaic output fluctuates, load demand changes, or carbon emission constraints are adjusted, existing methods are unable to adjust control strategies in a timely manner, thus restricting the park's synergistic optimization of operation under the goals of economy and low carbon. Summary of the Invention

[0006] This invention provides a method, device, system, and medium for regulating flexible resources in a park, which can solve at least one of the above-mentioned technical problems.

[0007] In a first aspect, embodiments of the present invention provide a method for regulating flexible resources in a park, comprising: Acquire flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices in the park at various times during the control cycle; Based on the flexible resource data mentioned above, a multi-objective static optimization model is constructed with the objectives of minimizing grid power purchase costs and carbon emissions, maximizing renewable energy absorption rate and power supply reliability. Based on the first constraint of the park, the multi-objective static optimization model is solved by a non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set; With the goals of maximizing carbon emission benefits and minimizing electricity costs in the industrial park, a multi-objective dynamic optimization model is constructed, wherein the dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by the candidate control solutions in the Pareto optimal solution set; Based on the second constraint of the park, the multi-objective dynamic optimization model is solved to obtain a flexible resource regulation scheme, wherein the flexible resource regulation scheme includes the power purchased by the power grid, the energy storage charging and discharging power, and the air conditioning load regulation power of the park at each time during the regulation period.

[0008] Secondly, embodiments of the present invention provide a device for regulating flexible resources in a park, comprising: The data acquisition module is used to acquire flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices in the park at various times during the control cycle; The static optimization model construction module is used to construct a multi-objective static optimization model based on the various flexible resource data, with the objectives of minimizing grid power purchase cost and carbon emissions, maximizing renewable energy absorption rate and power supply reliability. The static optimization model solving module is used to solve the multi-objective static optimization model based on the first constraint condition of the park, and obtain the Pareto optimal solution set by using a non-dominated sorting genetic algorithm. The dynamic optimization model construction module is used to construct a multi-objective dynamic optimization model with the objectives of maximizing carbon emission benefits and minimizing electricity costs in the park. The dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by the candidate control solutions in the Pareto optimal solution set. The dynamic optimization model solving module is used to solve the multi-objective dynamic optimization model based on the second constraint condition of the park to obtain a flexible resource regulation scheme. The flexible resource regulation scheme includes the power purchased by the power grid, the energy storage charging and discharging power, and the air conditioning load regulation power of the park at each time during the regulation period.

[0009] Thirdly, embodiments of the present invention also provide a system for regulating flexible resources in a park, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in any one of the embodiments of the present invention.

[0010] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the method described in any one of the embodiments of the present invention.

[0011] This invention employs a technical solution to acquire flexible resource data for photovoltaic power generation devices, energy storage devices, and air conditioning load devices in a park at various times within a control cycle. Based on this, firstly, a multi-objective static optimization model is constructed with the objectives of minimizing grid purchase costs and carbon emissions, and maximizing renewable energy absorption rate and power supply reliability. Secondly, using the park's first constraint as the solution boundary of the multi-objective static optimization model, a non-dominated sorting genetic algorithm is used to solve the model, obtaining a Pareto optimal solution set. This provides multiple feasible global optimization candidate schemes for the park's flexible resource control. Then, with the objectives of maximizing the park's carbon emission benefits and minimizing electricity costs, a multi-objective dynamic optimization model is constructed, and the dynamic priority coefficients in the multi-objective dynamic optimization model are adaptively adjusted based on the candidate control solutions in the Pareto optimal solution set. Finally, using the park's second constraint as the solution boundary of the multi-objective dynamic optimization model, the model is solved to obtain the park's flexible resource control scheme. Thus, resource synergistic optimization and adaptive operation of the park can be achieved under the objectives of economic efficiency and low carbon emissions.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of the invention. Wherein: Figure 1 This is a flowchart of a method for regulating flexible resources in a park according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a control device for flexible resources in a park according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of an electronic device used to implement the methods of embodiments of the present invention. Detailed Implementation

[0014] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0015] Figure 1 This is a flowchart of a method for regulating flexible resources in a park according to an embodiment of the present invention.

[0016] like Figure 1As shown, the methods for regulating flexible resources in this park may include: S110, acquires flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices in the park at each moment during the control cycle; S120, based on various flexible resource data, constructs a multi-objective static optimization model with the objectives of minimizing grid power purchase costs and carbon emissions, maximizing renewable energy absorption rate and power supply reliability; S130, based on the first constraint of the park, solves the multi-objective static optimization model by non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set; S140, with the goal of maximizing carbon emission benefits and minimizing electricity costs in the park, constructs a multi-objective dynamic optimization model. The dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by the candidate control solutions in the Pareto optimal solution set. S150, based on the second constraint of the park, solves the multi-objective dynamic optimization model to obtain a flexible resource regulation scheme, which includes the power purchased by the power grid, the charging and discharging power of energy storage, and the regulating power of air conditioning load at each moment in the regulation cycle of the park.

[0017] For example, the park's energy management system establishes data acquisition connections with photovoltaic power generation devices (e.g., photovoltaic panels), energy storage devices (e.g., water storage, water pump heat pumps, and park boilers), and air conditioning load devices (e.g., air conditioners), respectively, and reads operational data at each moment in real time or near real time from the corresponding monitoring terminals or control interfaces. Specifically, for photovoltaic power generation devices, data such as their actual power generation, predicted power generation, and upper limit output are acquired at each moment; for energy storage devices, data such as their state of charge, charging and discharging power, maximum charging and discharging capacity, and charging and discharging efficiency are acquired at each moment; and for air conditioning load devices, data such as their power consumption, adjustable power range, indoor temperature setpoint, and comfort constraint parameters are acquired at each moment. After data collection is completed, the data from different devices are time-aligned and format-unified. The flexible resource data corresponding to each device at the same time is mapped to a unified time series. Outliers or missing values ​​are corrected or filled in. Finally, flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices covering each time point in the control cycle are formed, providing basic input data for subsequent construction of optimization models.

[0018] For example, the control period can be measured in years, months, or days. Each moment in the control period can be measured in seconds, minutes, or hours.

[0019] For example, after acquiring the corresponding flexible resource data, in order to quantify the operating characteristics, constraint boundaries and adjustable potential of each flexible resource (photovoltaic power generation, hydropower storage, air conditioning, etc.), a corresponding mathematical model can be established, and each data model can be trained to output the predicted value of each flexible resource at each moment within the control cycle (e.g., the predicted value of power generation per hour in the next 24 hours), so as to provide accurate optimization variables and constraints for subsequent multi-objective optimization problems.

[0020] In this example, the photovoltaic power generation of the photovoltaic power generation device can be modeled and represented as: In the formula, Photovoltaic power generation; Photovoltaic panel conversion efficiency; for; For real-time monitoring of light intensity; Temperature coefficient; To monitor ambient temperature in real time; For reference temperature (e.g., (degrees Celsius).

[0021] In this example, the physical model corresponding to the energy storage device can be represented as: In the formula, for; Energy storage at all times; The stored energy at time t; This represents the hourly heat loss rate. Charging efficiency; The charging power at time t; For time steps (e.g., 1 hour, 15 minutes, etc.); Discharge efficiency; Let be the discharge power at time t.

[0022] To correlate stored energy with actual physical quantities, the following physical relationship is introduced to describe the transition between energy storage state and water temperature: In the formula: It refers to the water mass in the storage tank (100,000 kg). It is the specific heat capacity of water ( (kilojoules per kilogram per degree Celsius) The water temperature at time t, It is a reference temperature (e.g., ).

[0023] To ensure the feasibility of the physical model corresponding to the energy storage device in actual operation, the following constraints are set to limit the operational range of energy storage and release: First, power constraints: Second, energy storage capacity constraints: Third, charge and discharge mutual exclusion constraint: In the formula: It is the maximum charging power (500 kW). It is the maximum discharge power (1000 kW). It is the maximum energy storage capacity (7000 kWh). It is the minimum energy storage level.

[0024] In this example, the physical model corresponding to the air conditioning load device can be represented as: In the formula, For air conditioning energy consumption; It is the temperature difference coefficient; The temperature difference between indoors and outdoors; The coefficient for the number of people; For the number of people; This is a constant term.

[0025] By setting comfort constraints and combining them with historical data analysis, the range of load reduction that can be achieved under different conditions can be quantified. Comfort constraints and load reduction analysis are core considerations in the process of adjusting air conditioning load. By deploying smart meters that record energy consumption every 15 minutes in the office area of ​​the park, and combining this with approximately 8760 sets of hourly historical data from the past year, an upper limit of 28℃ for indoor temperature was set as a comfort constraint to ensure that the user experience is not affected after adjustment. Based on historical data statistics, under conditions of a temperature difference of 5-15℃ and 20-100 people, the energy consumption range is 20-50kW, with a conservative reduction rate of 20% and an aggressive reduction rate of up to 50%, the specific range being dynamically adjusted according to real-time conditions.

[0026] Having defined comfort constraints and load reduction potential, the following mathematical expression is introduced to quantify the energy consumption reduction in order to accurately calculate the adjustable range of the air conditioner: In the formula, Energy consumption that can be reduced; This represents the current energy consumption of the air conditioner. To reduce the proportion.

[0027] The key factors affecting k are: In the formula, For temperature difference; For the number of people; The hard standard for comfort constraints (the specific value can be set according to actual needs, and this example does not limit it).

[0028] For example, in step S120, the multi-objective static optimization model constructed with the objectives of minimizing grid power purchase costs and carbon emissions, maximizing renewable energy absorption rate and power supply reliability can be represented by the following functional expression.

[0029] First, minimize the cost of purchasing electricity from the power grid: In the formula, This is a function of the grid's electricity purchase cost; For the regulation cycle; The grid purchase price of electricity at time t; Let t be the power purchased by the power grid. For; the unit operation and maintenance cost of energy storage charging and discharging; and Let be the energy storage charging and discharging power at time t; Unit cost for air conditioning load regulation; Adjust power for air conditioning load.

[0030] Second, minimize carbon emissions: In the formula, It is a function of carbon emissions; The dynamic carbon emission factor of the power grid at time t; The carbon emission factor throughout the entire life cycle of energy storage charging and discharging; It is the reciprocal of the charging efficiency; This refers to the discharge efficiency.

[0031] Third, maximize the renewable energy integration rate: ; In the formula, The function is the renewable energy absorption rate. Let t be the actual photovoltaic power consumption rate at time t; The portion of the electricity purchased from the grid at time t that is renewable energy; Let t be the total power generation of the power grid at time t.

[0032] Fourth, maximize power supply reliability: ; In the formula, This is a power supply reliability function; Let t be the total load demand power of the park at time t.

[0033] In one implementation, the first constraints include: grid power purchase restrictions, energy storage charging and discharging constraints, and adjustable load reduction constraints under peak supply guarantee scenarios; grid power purchase restrictions, energy storage charging and discharging constraints, and adjustable load reduction constraints under off-peak consumption scenarios; a first power balance constraint; air conditioning adjustable range constraints; and photovoltaic output constraints.

[0034] For example, peak supply guarantee scenarios, such as 9:00-12:00 and 18:00-21:00.

[0035] The restrictions on power grid purchases can be expressed as: In the formula, the power purchased by the power grid at time t is... This represents the maximum power purchase capacity.

[0036] Energy storage charge and discharge constraints can be expressed as: In the formula, is the proportionality coefficient, where If the value is greater than or equal to 0.6, energy storage should be used to ensure power supply.

[0037] Adjustable load shedding constraints can be expressed as: In the formula, The baseline power of the adjustable load at time t (this value can be set manually according to actual needs).

[0038] For example, off-peak consumption scenarios, such as 12:00-14:00 and 21:00-0:00.

[0039] Restrictions on power grid purchases can be expressed as: In the formula, This is the minimum power purchase capacity.

[0040] Energy storage charge and discharge constraints can be expressed as: ;in, This is the proportionality coefficient. The value is greater than or equal to 0.8, which means that energy storage and charging are given priority.

[0041] Adjustable load shedding constraints can be expressed as: .

[0042] The first power balance constraint can be expressed as: In the formula: This refers to the actual power of the air conditioner, and it has... ; The amount of adjustment and reduction of air conditioning load at time t; Let t be the baseline power of the air conditioning load.

[0043] The adjustable range constraint of an air conditioner can be expressed as: In the formula: This represents the maximum reduction ratio (ranging from 0 to 0.5).

[0044] Photovoltaic output constraints can be expressed as: ; The maximum theoretical output of photovoltaic power at time t; This represents the actual power generation of the photovoltaic system.

[0045] In one implementation, a Pareto optimal solution set is obtained by solving a multi-objective static optimization model using a non-dominated sorting genetic algorithm. This includes: initializing a population, where the population comprises multiple individuals, each corresponding to a candidate regulation solution. The candidate regulation solution includes at least the power purchased by the power grid, the energy storage charging and discharging power, and the air conditioning load regulation power at each time point within the regulation cycle. Based on the multi-objective static optimization model, the objective function value is calculated for each individual in the population. The individuals in the population are then sorted using a non-dominated algorithm, and a corresponding non-dominated layer is assigned to each individual. Based on the distribution of individuals in the non-dominated layer within the objective space, the ownership of each individual is calculated. Crowding distance; Based on the results of non-dominated sorting and crowding distance, individuals are selected from the population to construct the parent population. Based on the preset crossover and mutation probabilities, crossover and mutation operations are performed on individuals in the parent population to generate the offspring population. The offspring population is merged with the parent population, and individuals are selected from the merged population based on the results of non-dominated sorting and crowding distance to form the population for the next iteration. When the number of iterations reaches the preset number or the change in the objective function value of individuals in the population meets the preset convergence condition, the iteration stops, and the candidate control solutions corresponding to each individual in the first non-dominated layer in the population at the time of stopping the iteration are taken as the Pareto optimal solution set.

[0046] For example, a Pareto optimal solution set refers to a set of non-dominant candidate solutions in a multi-objective optimization problem, where any solution cannot improve any other objective without worsening at least one objective. In other words, in this solution set, there is no solution that is not inferior to another solution in all optimization objectives and is strictly superior to that other solution in at least one objective. The Pareto optimal solution set characterizes the trade-off boundaries between multiple objectives, providing a basis for decision-makers to make choices under different optimization preferences.

[0047] For example, taking the flexible resource regulation of a park as an example, assuming that the multi-objective static optimization model simultaneously considers two objectives: "minimizing the cost of purchasing electricity from the power grid" and "minimizing carbon emissions," the following three candidate regulation solutions are obtained after solving the problem using a non-dominated sorting genetic algorithm: Solution A: The total electricity purchase cost during the regulation period is 1 million yuan, and the carbon emissions are 80 tons; Solution B: The total electricity purchase cost during the regulation period is 1.05 million yuan, and the carbon emissions are 70 tons; Solution C: The total electricity purchase cost during the regulation period is 980,000 yuan, and the carbon emissions are 95 tons.

[0048] Solution A has a lower electricity purchase cost than solution B, but higher carbon emissions; compared to solution C, it has lower carbon emissions, but higher electricity purchase cost. Therefore, solution A is not dominated by solutions B or C. Similarly, solution B is better than solutions A and C in terms of carbon emissions, but has a higher electricity purchase cost; solution C has the lowest electricity purchase cost, but the highest carbon emissions. There is no relationship of "being better in two objectives at the same time" among the three. Therefore, solutions A, B, and C together constitute the Pareto optimal solution set of this multi-objective static optimization model.

[0049] For example, firstly, the time scale of the control cycle is determined, such as a 24-hour control cycle divided into 24 time periods of 1 hour. Then, based on the park's historical operating data and equipment rated parameters, multiple feasible control schemes are randomly or semi-randomly generated as initial individuals. Each individual contains the grid power purchase sequence, the energy storage charging and discharging power sequence at each time period, and the air conditioning load adjustment power sequence at each time period. Finally, individuals that do not meet the power upper and lower limits, energy storage capacity constraints, or comfort constraints are eliminated through constraint checks, thus forming the initial population.

[0050] For example, the candidate control solution corresponding to each individual is substituted into a pre-constructed multi-objective static optimization model to calculate the target indicators such as total electricity purchase cost, total carbon emissions, renewable energy absorption rate, and power supply reliability for that individual during the control period. Then, these indicators are used as the objective function values ​​for that individual in the objective space.

[0051] For example, for a given individual, the total electricity purchase cost can be obtained by multiplying its 24-hour electricity purchase power sequence with the time-of-use electricity price and summing the results. The carbon emissions are calculated based on the purchased power and the grid carbon emission factor. At the same time, the renewable energy absorption rate is calculated by combining the photovoltaic output with the load matching degree, thus forming a set of objective function values ​​corresponding to that individual.

[0052] For example, non-dominated relation judgment can be performed based on the multi-objective function values ​​of each individual. Individuals that are not inferior to other individuals in all objectives and are superior to other individuals in at least one objective are classified into the first non-dominated layer, and the remaining individuals are classified into higher-level non-dominated layers in sequence. Within the same non-dominated layer, individuals are sorted according to each objective dimension, and the objective distance between adjacent individuals is calculated, thereby obtaining the crowding distance that reflects the sparsity of the individual distribution.

[0053] In this example, when calculating the target distance, the ranking results of each individual in the non-dominated layer within the target space can be used as a basis. For each objective function, the difference in objective function value between each individual and its neighboring individuals can be calculated, and the difference can be normalized. The normalized differences under each objective function are accumulated to obtain the crowding distance of each individual, which characterizes the sparseness of the distribution of each individual in the target space.

[0054] For example, individuals with lower non-dominant levels and larger crowding distances can be preferentially selected to enter the parent population. Then, two parent individuals are randomly selected according to a preset crossover probability, and their corresponding regulation power sequences are cross-recombined in the time dimension or device dimension. At the same time, the power purchase, energy storage charging and discharging power or air conditioning regulation power at certain moments are slightly perturbed according to the mutation probability to generate new offspring individuals.

[0055] For example, a maximum number of iterations and a threshold for the change in the objective function value can be preset. When the algorithm reaches the maximum number of iterations, or when the change in the objective function value of individuals in the first non-dominated layer is lower than the threshold for several consecutive generations, the algorithm is considered to have converged and iteration stops. The control schemes corresponding to all individuals in the first non-dominated layer at the time of iteration stop are output as the Pareto optimal solution set.

[0056] According to the above implementation method, firstly, candidate control solutions including grid power purchase, energy storage charging and discharging power, and air conditioning load regulation power are used as individuals to initialize the population, and the objective function of each individual is evaluated based on a multi-objective static optimization model. Then, through non-dominated sorting and congestion distance calculation, the superiority and diversity of solutions are simultaneously characterized in the objective space. Based on this, individual selection, crossover, and mutation operations are completed, and the population is updated iteratively by merging parent and child generations. Finally, when the iteration number or convergence condition is met, the candidate control solutions corresponding to the first non-dominated layer are extracted as the Pareto optimal solution set. In this way, not only can the optimal solution frontier be effectively approximated under multi-objective constraints, but the uniformity of the solution set distribution in the objective space can also be maintained, thus providing a more comprehensive, flexible, and selectable basis for flexible resource regulation decisions for the park among multiple objectives such as economy, low carbon emissions, and operational safety.

[0057] In one implementation, a multi-objective dynamic optimization model is constructed with the objectives of maximizing carbon emission benefits and minimizing electricity costs in the industrial park. The dynamic priority coefficients in this model are controlled by candidate control solutions in the Pareto optimal solution set. This includes: obtaining real-time time-of-use electricity prices and the real-time carbon emission factor of the power grid at each moment within the control period; dynamically setting strategic weights based on the strategies represented by the candidate control solutions in the Pareto optimal solution set; and weighting and summing the normalized value of the real-time carbon emission factor with the normalized value of the real-time time-of-use electricity price based on the strategic weights to obtain the normalized carbon economic cost; and then, based on the strategic weights and the real-time carbon emission factor... The dynamic priority coefficient is determined by multiplying the normalized values ​​of the carbon emissions by the normalized carbon economic cost. Based on the dynamic priority coefficient, the carbon emission factor at each time point, the power purchased from the grid, and the real-time time-of-use electricity price, the dynamic priority objective function is calculated. Based on the sum of the dynamic priority objective function, the direct carbon emission reduction function, and the indirect carbon emission reduction function, the total carbon emission objective function is determined, and its maximum value is calculated to construct a carbon emission benefit optimization sub-model. An economic benefit optimization sub-model is constructed with the goal of minimizing electricity costs. Based on the carbon emission benefit optimization sub-model and the economic benefit optimization sub-model, a multi-objective dynamic optimization model is determined.

[0058] For example, before the start of the control cycle, the time granularity and time range of the control cycle are first determined, such as dividing the control cycle into multiple moments with time intervals of 15 minutes or 1 hour. Then, by establishing a data interface connection with the electricity market trading system or grid dispatching platform, real-time time-of-use (TOU) electricity price data for each moment within the corresponding control cycle is obtained. The real-time TOU price can be the day-ahead price, the real-time price, or the settlement price after market clearing. Simultaneously with obtaining the price data, the real-time carbon emission factor corresponding to each moment is obtained through the grid carbon emission monitoring platform or the power industry carbon emission database. This carbon emission factor is used to characterize the carbon emission intensity per unit of electricity. Further, the obtained real-time TOU electricity price data and real-time carbon emission factor undergo time alignment, missing value completion, and outlier correction processing to ensure they correspond one-to-one with each moment within the control cycle in the time dimension. Finally, the processed real-time TOU electricity price and grid real-time carbon emission factor are used as the basic parameter inputs for subsequent multi-objective static optimization models and multi-objective dynamic optimization models to characterize the economic cost level and low-carbon constraint strength at different control moments.

[0059] For example, the calculation process of the dynamic priority coefficient can be represented by the following function expression: In the formula, The dynamic priority coefficient at time t; The real-time carbon emission factor of the power grid at time t; The maximum carbon emission factor within the regulation cycle; As a strategic weight; Normalized carbon economic cost; For real-time time-of-use electricity pricing; This refers to the highest time-of-use electricity price during the regulation period.

[0060] For example, the calculation process of the dynamic priority objective function can be expressed by the following function expression: In the formula, The objective function is a dynamic priority function. For the regulation cycle; Let be the dynamic priority coefficient at time t, where ; The adjustment factor at time t; Let be the carbon emission factor at time t; Let t be the power purchased from the grid; This is for real-time time-of-use electricity pricing.

[0061] In this example, the calculation process of the adjustment factor can be represented by a function expression as follows: In the formula, The adjustment factor at time t; Let be the carbon emission factor at time t; The maximum carbon emission factor within the regulation cycle; Let t be the power purchased from the grid; This refers to the maximum power purchased by the power grid during the regulation period.

[0062] For example, the direct carbon emission reduction function is shown below: ; In the formula, The function represents the direct carbon emission reduction; T represents the control period. The dynamic carbon emission factor of the power grid at time t; The power purchased from the grid at time t; The equivalent carbon emission factor for photovoltaic power generation; Let t be the power generation capacity of the photovoltaic system.

[0063] For example, the indirect carbon emission reduction function is shown below: ; In the formula, It is an indirect carbon emission reduction function; Carbon emission factors during peak power grid periods; The optimized load reduction achieved during peak power grid hours; The carbon emission factor throughout the entire life cycle of energy storage charging and discharging; Let t be the charging and discharging power of the stored energy.

[0064] For example, the functional expression of the carbon emission benefit optimization sub-model is as follows: ; In the formula, The objective function is the total carbon emissions. To find the maximum value of the objective function for total carbon emissions; It is a function of direct carbon emission reduction; It is an indirect carbon emission reduction function; This is a dynamic priority objective function.

[0065] It should be noted that in the formula The calculation expression for has been given in the previous example and will not be repeated here.

[0066] For example, the carbon emission benefit optimization sub-model shown in the aforementioned example ( ) and economic benefit optimization sub-model ( Together, they were identified as a multi-objective dynamic optimization model.

[0067] According to the above implementation method, by introducing real-time time-of-use electricity prices and real-time carbon emission factors, the static optimization results are combined with changes in the external environment during operation, enabling adaptive reconstruction of the park's flexible resource regulation objectives at the dynamic level. Specifically, on the one hand, based on the regulation preferences implied in the candidate regulation solutions in the Pareto optimal solution set, strategic weights are dynamically set, allowing the model to flexibly emphasize economic efficiency or low-carbon performance at different operational stages. On the other hand, by normalizing and weighting real-time electricity prices and carbon emission factors, a dynamic priority coefficient reflecting the combined impact of "carbon and price" is constructed, and further introduced into the carbon emission benefit optimization sub-model and the economic benefit optimization sub-model, thereby forming a multi-objective dynamic optimization model that takes into account both carbon emission reduction effects and electricity costs. In this way, not only can real-time price signals and carbon intensity signals be fully utilized to guide the refined regulation of flexible resources, but also the dynamic adjustment and trade-off of regulation strategies can be achieved in complex and ever-changing operating environments, effectively improving the park's synergistic optimization level and overall operational adaptability between low-carbon and economic operation.

[0068] In one implementation, strategic weights are dynamically set based on the strategies represented by candidate control solutions in the Pareto optimal solution set, including: if the strategy represented by the candidate control solution in the Pareto optimal solution set is economic priority control, then the strategic weight is adjusted to a first weight value; if the strategy represented by the candidate control solution is low-carbon priority control, then the strategic weight is adjusted to a second weight value; if the strategy represented by the candidate control solution is economic and low-carbon equilibrium control, then the strategic weight is adjusted to a third weight value; wherein the first weight value is less than the third weight value, and the third weight value is less than the second weight value.

[0069] For example, the strategy type is determined for the multi-objective static optimization results corresponding to each candidate control solution in the Pareto optimal solution set. Specifically, this can be achieved by comparing the relative merits of the candidate control solution in the "electricity cost objective function" and the "carbon emission objective function". For instance, when the electricity cost objective value corresponding to a candidate control solution is in the first preset proportion range of the Pareto optimal solution set, while its carbon emission objective value is in the second preset proportion range, the candidate control solution is determined to be an economic priority control strategy; when the carbon emission objective value corresponding to a candidate control solution is in the first preset proportion range, while its electricity cost objective value is in the second preset proportion range, the candidate control solution is determined to be a low-carbon priority control strategy; when a candidate control solution is in the middle preset proportion range for both the electricity cost objective value and the carbon emission objective value, the candidate control solution is determined to be an economic and low-carbon equilibrium control strategy.

[0070] For example, in a solution set containing 100 Pareto optimal solutions, if a candidate control solution ranks in the top 20% for electricity costs and in the bottom 40% for carbon emissions, then the candidate control solution is identified as an economic priority control strategy.

[0071] For example, after identifying the strategy type, the strategic weights are adaptively adjusted according to the strategy type corresponding to the candidate control solution. Specifically, when the candidate control solution is determined to be an economic priority control strategy, the strategic weight is adjusted to the first weight value; when the candidate control solution is determined to be a low-carbon priority control strategy, the strategic weight is adjusted to the second weight value; and when the candidate control solution is determined to be an economic and low-carbon equilibrium control strategy, the strategic weight is adjusted to the third weight value.

[0072] For example, the first weight value is the smallest, the third weight value is the next smallest, and the second weight value is the largest. For instance, the first weight value can be set to 0.3, the third weight value to 0.5, and the second weight value to 0.7, so that the influence of strategic weights on carbon emission factors under different strategy modes shows an increasing relationship from weak to strong.

[0073] Understandably, after the strategic weights are adjusted, the determined strategic weights are used as key parameter inputs for the calculation of dynamic priority coefficients, carbon economic cost weighting, and construction of the carbon emission benefit objective function in the subsequent multi-objective dynamic optimization model. This allows the dynamic optimization process to reflect differentiated economic and low-carbon emphases under different control strategies.

[0074] According to the above implementation method, by adaptively adjusting the strategic weights based on the strategy types represented by the candidate control solutions in the Pareto optimal solution set, and forming an ordered gradient relationship between the first weight value, the third weight value, and the second weight value, this invention can effectively transfer the strategy preferences obtained in the multi-objective static optimization stage to the multi-objective dynamic optimization stage. This avoids the control strategy mismatch problem caused by the fixed target weights in the dynamic optimization process, and enables flexible switching between multiple control modes such as economic priority, low-carbon priority, and economic and low-carbon balance. It improves the adaptability and decision-making rationality of flexible resource control in the park under different operating scenarios, and is conducive to further improving the comprehensive performance of economic benefits and carbon emission reduction benefits while ensuring operational safety.

[0075] In one implementation, an economic benefit optimization sub-model is constructed with the goal of minimizing electricity costs. This includes: summing the electricity purchase costs at each time point within the control period to obtain the total electricity purchase cost function corresponding to the control period; subtracting the dynamic priority objective function from the sum of the total electricity purchase cost function and the cost function of the park purchasing electricity from the grid to determine the total electricity consumption objective function; and solving for the minimum value of the total electricity consumption objective function to construct the economic benefit optimization sub-model.

[0076] For example, an economic benefit optimization sub-model is constructed with the goal of minimizing electricity costs. The economic benefit optimization sub-model can be as follows: ; In the formula, Optimize the sub-model for economic benefits; The objective function is a dynamic priority function. The total cost of electricity purchase during the regulation period; This is a function of the grid's electricity purchase cost; Let be the objective function for total electricity consumption.

[0077] According to the above implementation method, by accumulating the electricity purchase costs at each moment within the control cycle, a total electricity purchase cost function covering the entire control cycle is formed, comprehensively depicting the economic expenditure required for electricity consumption in the park from a time dimension. Based on this, the total electricity purchase cost function and the park's basic cost function for purchasing electricity from the grid are modeled together, and a dynamic priority objective function is introduced as an adjustment term. By summing the cost functions and subtracting the dynamic priority objective function, a total electricity consumption objective function reflecting different control strategy preferences is constructed. Furthermore, by minimizing the total electricity consumption objective function, an economic benefit optimization sub-model oriented towards optimal overall electricity cost is formed. In this way, not only can the time-cumulative effect of electricity purchase costs be comprehensively considered across the entire control cycle, but also, through the dynamic priority objective function, control preferences such as economic efficiency and low carbon emissions are embedded into the cost optimization process. This results in a control outcome that is no longer a simple static low-price electricity purchase scheme, but a control scheme that balances real-time strategy weight adjustments and optimal overall economic benefits, thereby improving the economic rationality and adaptability of the park's flexible resource control in complex operating environments.

[0078] In one implementation, the second constraint includes: a second power balance constraint, an energy storage state constraint, an energy storage capacity and power constraint, a flexible load adjustment constraint, and a carbon emission tracking constraint.

[0079] For example, the second power balance constraint: In the formula, Let t be the total load demand power of the park. Let t be the photovoltaic power generation capacity. Let t be the discharge power of the energy storage device; The charging power of the energy storage device at time t; Let t be the power purchased from the grid.

[0080] For example, the energy storage state constraint can be expressed as a function: ; In the formula, The stored energy at time t+1; The stored energy at time t; Self-discharge rate or energy loss rate; For charging efficiency; For discharge efficiency; For time step; The charging power at time t; Let be the discharge power at time t.

[0081] For example, the energy storage capacity and power constraints can be expressed as a function: In the formula, Minimum energy storage capacity; Let be the energy storage capacity at time t; This represents the maximum energy storage capacity. This is the maximum charging power; The charging power at time t; Let be the discharge power at time t; This represents the maximum discharge power.

[0082] For example, the flexible load adjustment constraint can be expressed as a function: ; In the formula, This represents the actual operating power of the flexible load. For time step; To determine the minimum total energy demand of flexible loads within the regulation cycle; Let t be the minimum allowable operating power of the flexible load; Let t be the allowable operating power of the flexible load; Let t be the maximum allowable operating power of the flexible load.

[0083] For example, carbon emission tracking constraints can be expressed as a functional expression: ; In the formula, The dynamic carbon emission factor of the power grid at time t; The power purchased by the power grid at time t; This represents the maximum allowable carbon emissions at time t. Carbon emission tracking constraints apply only to all periods defined as "high carbon emissions". Effective immediately.

[0084] In one implementation, a particle swarm optimization algorithm can be used to solve the multi-objective dynamic optimization model to obtain a flexible resource regulation scheme.

[0085] This can be understood as a set of specific, immediately executable control instructions generated within the framework of a dynamic optimization model for an upcoming very short time period (e.g., the next hour or 15 minutes).

[0086] For example, if at 9:00 AM on a certain day, low-carbon priority is determined as the guiding strategy for the day based on the candidate control solutions in the Pareto optimal solution set, then the flexible resource control scheme obtained by executing the dynamic optimization model building module at 9:00 AM to 10:00 AM could include: discharging at a power of 850 kW from 9:00 AM to 10:00 AM; reducing the total air conditioning load by 90 kW from 9:00 AM to 10:00 AM; and purchasing 400 kW of electricity from the grid from 9:00 AM to 10:00 AM.

[0087] Specifically, firstly, based on the decision variables to be optimized in the multi-objective dynamic optimization model, a position vector for each particle is constructed. Each particle's position vector corresponds to a candidate flexible resource regulation scheme, and the position vector includes at least the grid power purchase, energy storage charging and discharging power, and air conditioning load regulation power at each moment within the regulation period. A corresponding velocity vector is then randomly initialized for each particle.

[0088] Subsequently, the position vectors of each particle obtained from the initialization are substituted into the multi-objective dynamic optimization model. Under the premise of satisfying the second constraint, the target values ​​of the carbon emission benefit optimization sub-model and the economic benefit optimization sub-model for each particle in the current iteration are calculated respectively. Based on the multi-objective evaluation criteria or the weighted fitness function, the comprehensive fitness value of each particle is determined.

[0089] Next, the current fitness of each particle is compared with its historical best fitness. If the current fitness is better than the historical best fitness, the individual best position of that particle is updated. Simultaneously, the individual best positions of all particles in the entire particle swarm are compared, and the position of the particle with the best overall fitness is selected as the global best position. Then, according to the velocity update formula of the particle swarm optimization algorithm, combined with the particle's current velocity, current position information, individual best position, and global best position, the velocity vector of each particle is updated. Based on the updated velocity vector, the position vector of each particle is updated. Boundary checks and constraint corrections are performed on the updated positions to ensure that the updated control power meets the park's operational constraints.

[0090] Based on this, the process of fitness calculation, individual optimal and global optimal update, and particle position and velocity update is repeated until the preset maximum number of iterations is reached or the overall fitness change of the particle swarm meets the convergence condition (for example, in two adjacent iterations, the change in the objective function value corresponding to the best particle in the particle swarm is less than or equal to the preset threshold).

[0091] Finally, the position vector corresponding to the globally optimal particle obtained at the end of the iteration is used as the optimal solution output of the multi-objective dynamic optimization model, thereby obtaining the flexible resource regulation scheme composed of the power purchased by the power grid, the charging and discharging power of energy storage, and the regulating power of air conditioning load at each moment in the regulation cycle of the park.

[0092] Figure 2 This is a structural block diagram of a control device for flexible resources in a park according to an embodiment of the present invention.

[0093] like Figure 2 As shown, the flexible resource control device for this park may include: The data acquisition module 510 is used to acquire flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices in the park at various times during the control cycle. The static optimization model construction module 520 is used to construct a multi-objective static optimization model based on the various flexible resource data, with the objectives of minimizing grid power purchase cost and carbon emissions, maximizing renewable energy absorption rate and power supply reliability. The static optimization model solving module 530 is used to solve the multi-objective static optimization model based on the first constraint condition of the park, and obtain the Pareto optimal solution set by using a non-dominated sorting genetic algorithm. The dynamic optimization model construction module 540 is used to construct a multi-objective dynamic optimization model with the objectives of maximizing carbon emission benefits and minimizing electricity costs in the park. The dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by the candidate control solutions in the Pareto optimal solution set. The dynamic optimization model solving module 550 is used to solve the multi-objective dynamic optimization model based on the second constraint condition of the park to obtain a flexible resource regulation scheme. The flexible resource regulation scheme includes the power purchased by the power grid, the energy storage charging and discharging power, and the air conditioning load regulation power of the park at each time during the regulation period.

[0094] In one implementation, the dynamic optimization model construction module includes: The acquisition unit is used to acquire the real-time time-of-use electricity price and the real-time carbon emission factor of the power grid at each moment during the control cycle. The strategic weight dynamic setting unit is used to dynamically set strategic weights according to the strategies represented by the candidate control solutions in the Pareto optimal solution set, and to perform a weighted summation of the normalized value of the real-time carbon emission factor and the normalized value of the real-time time-of-use electricity price based on the strategic weights to obtain the normalized carbon economic cost. The dynamic priority coefficient determination unit is used to determine the dynamic priority coefficient based on the ratio of the product of the strategic weight and the normalized value of the real-time carbon emission factor to the normalized carbon economic cost. The dynamic priority objective function calculation unit is used to calculate the dynamic priority objective function based on the dynamic priority coefficient, the carbon emission factor at each time, the power purchased from the grid, and the real-time time-of-use electricity price. The carbon emission benefit optimization sub-model construction unit is used to determine the total carbon emission objective function based on the sum of the dynamic priority objective function, the direct carbon emission reduction function, and the indirect carbon emission reduction function, and to solve for the maximum value of the total carbon emission objective function in order to construct the carbon emission benefit optimization sub-model. The economic benefit optimization sub-model construction unit is used to construct an economic benefit optimization sub-model with the goal of minimizing electricity costs. The multi-objective dynamic optimization model determination unit is used to determine the multi-objective dynamic optimization model based on the carbon emission benefit optimization sub-model and the economic benefit optimization sub-model.

[0095] In one embodiment, the strategic weight dynamic setting unit includes: The first adjustment subunit is used to adjust the strategic weight to a first weight value if the strategy represented by the candidate control solution in the Pareto optimal solution set is economic priority control. The second subunit is used to adjust the strategic weight to a second weight value if the strategy represented by the candidate control solution is low-carbon priority control. The third subunit is used to adjust the strategic weight to a third weight value if the strategy represented by the candidate control solution is economic and low-carbon equilibrium control; wherein the first weight value is less than the third weight value, and the third weight value is less than the second weight value.

[0096] In one implementation, the economic benefit optimization sub-model construction unit includes: The summation subunit is used to sum the electricity purchase costs at each time point within the control period to obtain the total electricity purchase cost function corresponding to the control period. The total electricity consumption target function determination subunit is used to determine the total electricity consumption target function by subtracting the dynamic priority target function from the sum of the total electricity purchase cost function and the cost function of the park purchasing electricity from the grid. The solution sub-unit is used to find the minimum value of the total electricity consumption objective function in order to construct the economic benefit optimization sub-model.

[0097] In one embodiment, the static optimization model solving module includes: An initial population unit is used to initialize the population, wherein the population includes multiple individuals, each individual corresponds to a candidate control solution, and the candidate control solution includes at least the power purchased by the grid, the energy storage charging and discharging power, and the air conditioning load adjustment power of the park at each time during the control period; The objective function value calculation unit is used to calculate the objective function value for each individual in the population based on the multi-objective static optimization model. The non-dominated sorting unit is used to perform non-dominated sorting on each individual in the population, assign a corresponding non-dominated layer to each individual, and calculate the crowding distance of each individual based on the distribution of each individual in the non-dominated layer in the target space. A population construction unit is used to select individuals from the population based on the result of the non-dominated sorting and the crowding distance to construct a parent population, and to perform crossover and mutation operations on the individuals in the parent population based on preset crossover and mutation probabilities to generate a child population. The population merging unit is used to merge the offspring population with the parent population, and select individuals from the merged population based on the non-dominated sorting result and the crowding distance to form the population for the next iteration. The iteration termination unit is used to stop the iteration when the number of iterations reaches a preset number or the change in the objective function value of an individual in the population meets a preset convergence condition, and to take the candidate control solutions corresponding to each individual in the first non-dominated layer of the population at the time of stopping the iteration as the Pareto optimal solution set.

[0098] In one implementation, the first constraint includes: grid power purchase restrictions, energy storage charging and discharging constraints, and adjustable load reduction constraints under peak supply guarantee scenarios; grid power purchase restrictions, energy storage charging and discharging constraints, and adjustable load reduction constraints under off-peak consumption scenarios; a first power balance constraint; air conditioning adjustable range constraints; and photovoltaic output constraints.

[0099] In one embodiment, the second constraint includes: a second power balance constraint, an energy storage state constraint, an energy storage capacity and power constraint, a flexible load adjustment constraint, and a carbon emission tracking constraint.

[0100] The specific functions and examples of each module and submodule of the system in this embodiment of the invention can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0101] The acquisition, storage, and application of user personal information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0102] This invention also provides a system for regulating flexible resources in a park, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in any one of the embodiments of the present invention.

[0103] The beneficial effects of the flexible resource regulation system in the park according to the present invention are equivalent to the beneficial effects of the above-mentioned flexible resource regulation method in the park, and will not be repeated here.

[0104] This invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the method described in any one of the embodiments of this invention.

[0105] The beneficial effects of the storage medium of the present invention are equivalent to the beneficial effects of the above-mentioned method for regulating flexible resources in the park, and will not be repeated here.

[0106] Figure 3 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present invention is shown. Electronic device 800 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 800 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0107] like Figure 3 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0108] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0109] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for regulating campus flexible resources. For example, in some embodiments, the method for regulating campus flexible resources can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the method for regulating campus flexible resources described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the method for regulating campus flexible resources by any other suitable means (e.g., by means of firmware).

[0110] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0114] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0115] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0116] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0117] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for regulating flexible resources in a park, characterized in that, include: Acquire flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices in the park at various times during the control cycle; Based on the flexible resource data mentioned above, a multi-objective static optimization model is constructed with the objectives of minimizing grid power purchase costs and carbon emissions, maximizing renewable energy absorption rate and power supply reliability. Based on the first constraint condition of the park, the multi-objective static optimization model is solved by a non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set; With the goals of maximizing carbon emission benefits and minimizing electricity costs in the industrial park, a multi-objective dynamic optimization model is constructed, wherein the dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by the candidate control solutions in the Pareto optimal solution set; Based on the second constraint of the park, the multi-objective dynamic optimization model is solved to obtain a flexible resource regulation scheme, wherein the flexible resource regulation scheme includes the power purchased by the power grid, the energy storage charging and discharging power, and the air conditioning load regulation power of the park at each time during the regulation period.

2. The method according to claim 1, characterized in that, The objective is to maximize carbon emission benefits and minimize electricity costs in the industrial park. A multi-objective dynamic optimization model is constructed, wherein the dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by candidate control solutions in the Pareto optimal solution set, including: Obtain real-time time-of-use electricity prices and real-time carbon emission factors of the power grid at each moment within the regulation cycle; Based on the strategies represented by the candidate control solutions in the Pareto optimal solution set, strategic weights are dynamically set, and the normalized value of the real-time carbon emission factor is weighted and summed with the normalized value of the real-time time-of-use electricity price based on the strategic weights to obtain the normalized carbon economic cost. The dynamic priority coefficient is determined by the ratio of the product of the strategic weight and the normalized value of the real-time carbon emission factor to the normalized carbon economic cost. Based on the dynamic priority coefficient, the carbon emission factor at each time point, the power purchased from the grid, and the real-time time-of-use electricity price, the dynamic priority objective function is calculated. Based on the sum of the dynamic priority objective function, the direct carbon emission reduction function, and the indirect carbon emission reduction function, the total carbon emission objective function is determined, and the maximum value of the total carbon emission objective function is solved to construct a carbon emission benefit optimization sub-model. With the goal of minimizing electricity costs, an economic benefit optimization sub-model is constructed; Based on the carbon emission benefit optimization sub-model and the economic benefit optimization sub-model, the multi-objective dynamic optimization model is determined.

3. The method of claim 2, wherein, The step of dynamically setting strategic weights based on the strategies represented by candidate control solutions in the Pareto optimal solution set includes: If the strategy represented by the candidate control solution in the Pareto optimal solution set is economic priority control, then the strategic weight is adjusted to the first weight value. If the strategy represented by the candidate control solution is low-carbon priority control, then the strategic weight is adjusted to the second weight value; If the strategy represented by the candidate control solution is economic and low-carbon equilibrium control, then the strategic weight is adjusted to a third weight value; wherein the first weight value is less than the third weight value, and the third weight value is less than the second weight value.

4. The method of claim 2, wherein, The economic benefit optimization sub-model, with the goal of minimizing electricity costs, includes: The total electricity purchase cost function corresponding to the control period is obtained by summing the electricity purchase costs at each time point within the control period. Based on the sum of the total electricity purchase cost function and the cost function of the park purchasing electricity from the grid, the dynamic priority objective function is subtracted to determine the total electricity consumption objective function; The minimum value of the total electricity consumption objective function is obtained to construct the economic benefit optimization sub-model.

5. The method of claim 1, wherein, The process of solving the multi-objective static optimization model using a non-dominated sorting genetic algorithm yields a Pareto optimal solution set, including: Initialize a population, wherein the population includes multiple individuals, each individual corresponds to a candidate control solution, and the candidate control solution includes at least the power purchased by the grid, the energy storage charging and discharging power, and the air conditioning load regulation power of the park at each time during the control period; Based on the multi-objective static optimization model, the objective function value is calculated for each individual in the population. The individuals in the population are sorted in a non-dominated order, and a corresponding non-dominated layer is assigned to each individual. Based on the distribution of each individual in the non-dominated layer in the target space, the crowding distance of each individual is calculated. Based on the results of the non-dominated sorting and the crowding distance, individuals are selected from the population to construct a parent population, and crossover and mutation operations are performed on the individuals in the parent population based on preset crossover and mutation probabilities to generate a child population. The offspring population is merged with the parent population, and individuals are selected from the merged population based on the non-dominated sorting result and the crowding distance to form the population for the next iteration. When the number of iterations reaches a preset number or the change in the objective function value of an individual in the population satisfies a preset convergence condition, the iteration stops, and the candidate control solutions corresponding to each individual in the first non-dominated layer of the population at the time of stopping the iteration are taken as the Pareto optimal solution set.

6. The method of claim 1, wherein, The first set of constraints includes: grid power purchase restrictions, energy storage charging and discharging constraints, and adjustable load reduction constraints under peak supply guarantee scenarios; grid power purchase restrictions, energy storage charging and discharging constraints, and adjustable load reduction constraints under off-peak consumption scenarios; first power balance constraints; air conditioning adjustable range constraints; and photovoltaic output constraints.

7. The method of claim 1, wherein, The second set of constraints includes: second power balance constraint, energy storage state constraint, energy storage capacity and power constraint, flexible load adjustment constraint, and carbon emission tracking constraint.

8. A park flexible resource regulation device, characterized in that, include: The data acquisition module is used to acquire flexible resource data of photovoltaic power generation devices, energy storage devices, and air conditioning load devices in the park at various times during the control cycle; The static optimization model construction module is used to construct a multi-objective static optimization model based on the various flexible resource data, with the objectives of minimizing grid power purchase cost and carbon emissions, maximizing renewable energy absorption rate and power supply reliability. The static optimization model solving module is used to solve the multi-objective static optimization model based on the first constraint condition of the park, and obtain the Pareto optimal solution set by using a non-dominated sorting genetic algorithm. The dynamic optimization model construction module is used to construct a multi-objective dynamic optimization model with the objectives of maximizing carbon emission benefits and minimizing electricity costs in the park. The dynamic priority coefficients in the multi-objective dynamic optimization model are controlled by the candidate control solutions in the Pareto optimal solution set. The dynamic optimization model solving module is used to solve the multi-objective dynamic optimization model based on the second constraint condition of the park to obtain a flexible resource regulation scheme. The flexible resource regulation scheme includes the power purchased by the power grid, the energy storage charging and discharging power, and the air conditioning load regulation power of the park at each time during the regulation period.

9. A park flexible resource regulation system, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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