Multi-level resource cooperative operation control method, device, equipment, medium and program product

Abstract

The invention discloses a multistage resource cooperative operation control method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring operation data of a photovoltaic system, a wind power system, an energy storage system, an electric vehicle and a coal-fired unit in an active power distribution network in a scheduling period; based on the obtained operation data, establishing mathematical models for the multi-level resources respectively; on the basis of the established mathematical model, constructing a double-layer optimization control model which takes the minimization of the total operation cost of the system as an upper-layer target and takes the minimization of the power imbalance index as a lower-layer target; and solving the constructed double-layer optimization control model by adopting a self-adaptive optimization algorithm introducing a self-adaptive weight and a chaos disturbance mechanism, and outputting an optimal multi-level resource cooperative operation control strategy. According to the invention, collaborative optimization operation of multi-level resources of the active power distribution network can be realized, and the power supply stability and the clean energy consumption capability are improved.

Description

Technical Field

[0001] This invention relates to a multi-level resource collaborative operation control method, device, equipment, medium, and program product, belonging to the field of power distribution network optimization operation technology. Background Technology

[0002] Against the backdrop of continuous changes in the global energy structure, the proportion of distributed renewable energy sources such as photovoltaics and wind power in the power system is steadily increasing, and flexible loads such as energy storage units and electric vehicles are also being connected at an accelerated pace, driving the evolution of traditional distribution networks into active distribution networks with bidirectional power flow and complex structures.

[0003] The development of active distribution networks has effectively alleviated energy shortages and environmental pressures, but it has also brought significant operational and control challenges. On the one hand, the types of resources in distribution networks are becoming increasingly diverse, encompassing multi-level heterogeneous units such as photovoltaics, wind power, energy storage systems, electric vehicles, and coal-fired units. These resources exhibit significant differences in physical characteristics and complex operational constraints. Existing scheduling methods often optimize only a single or a few types of resources, lacking a unified and refined modeling and coordination mechanism for all types of resources, making it difficult to achieve economical operation and stable control of the entire system. On the other hand, existing optimization models often focus solely on minimizing operating costs, failing to consider power balance as an equally important optimization objective. Under the influence of the volatility of high-proportion renewable energy sources, this can easily lead to increased risks of supply-demand imbalances, affecting power supply reliability. Furthermore, the collaborative optimization problem of active distribution networks is a complex mathematical problem involving high dimensions, nonlinearity, and multiple constraints. Traditional optimization algorithms generally suffer from slow convergence speeds and a tendency to get trapped in local optima when dealing with such problems, making it difficult to meet the computational efficiency and solution quality requirements of practical scheduling.

[0004] Therefore, there is an urgent need to propose a multi-level resource collaborative operation control method that can coordinate five core resources, namely photovoltaic, wind power, energy storage systems, electric vehicles and coal-fired units, take into account both economic efficiency and power balance, and has efficient and stable solution capabilities, so as to adapt to the complex operation requirements of active power distribution networks. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a multi-level resource collaborative operation control method, device, equipment, medium, and program product, which can realize the collaborative optimization operation of multi-level resources in active power distribution networks, improve power supply stability, and enhance the capacity for clean energy absorption. To achieve the above objective, this invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a multi-level resource collaborative operation control method, comprising:

[0007] Acquire operational data of photovoltaic, wind power, energy storage systems, electric vehicles, and coal-fired power units in the active distribution network during the scheduling period;

[0008] Based on the acquired operational data, mathematical models are established for each level of resources;

[0009] Based on the established mathematical model, a two-level optimization control model is constructed with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance index as the lower-level objective.

[0010] An adaptive optimization algorithm incorporating adaptive weights and chaotic perturbation mechanisms is employed to solve the constructed two-level optimization control model, outputting the optimal multi-level resource collaborative operation control strategy.

[0011] In conjunction with the first aspect, optionally, the step of establishing mathematical models for multi-level resources based on the acquired operational data includes:

[0012] The mathematical model for photovoltaics is based on the calculation of photovoltaic output based on solar radiation intensity and photoelectric conversion efficiency;

[0013] The mathematical model of wind power is a piecewise functional relationship between the output power of the wind turbine and the wind speed.

[0014] The mathematical model of the energy storage system includes a dynamic equation of the state of charge, which satisfies the charging and discharging power constraints and the state of charge constraints.

[0015] The mathematical model of the electric vehicle includes a disordered charging load model and an ordered charging load model;

[0016] The mathematical model of the coal-fired power unit is a quadratic function of the power generation cost.

[0017] In conjunction with the first aspect, optionally, based on the established mathematical model, a two-level optimization control model is constructed, with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance exponent as the lower-level objective, including:

[0018] The upper-level model aims to minimize the total operating cost of the system within a scheduling period T. The objective is expressed by the following formula:

[0019] ,

[0020] ,

[0021] ,

[0022] in, The operating cost of photovoltaic power generation includes basic operation and maintenance costs and curtailment costs. For basic operation and maintenance costs of photovoltaic systems, For abandoned light power, This is the light rejection factor; The operating cost of wind power generation includes basic operation and maintenance costs and wind curtailment costs. For basic operation and maintenance costs of wind power, For wind curtailment power, This is the wind curtailment coefficient. For the operating costs of energy storage systems, For the cost of power generation by coal-fired units, The cost of charging electric vehicles;

[0023] The lower-level model aims to minimize the power imbalance index and calculates the power imbalance degree index. It can be expressed by the following formula:

[0024] ,

[0025] in, T The total number of time periods in the scheduling cycle. for t Total power generation of the system at any time for t Total system load power at any given time.

[0026] In conjunction with the first aspect, optionally, the two-level optimization control model further includes constraints, which include:

[0027] Power balance constraints are used to ensure that the total generating capacity of the distribution network matches the total load capacity.

[0028] The ramp-up power constraint for coal-fired power units is used to limit the rate of change of output of coal-fired power units in adjacent time periods;

[0029] The upper and lower limits of output for each power source are used to restrict the output range of photovoltaic, wind power, energy storage systems and coal-fired units.

[0030] In conjunction with the first aspect, optionally, the adaptive optimization algorithm incorporating adaptive weights and chaotic perturbation mechanisms is used to solve the constructed two-layer optimization control model and output the optimal multi-level resource cooperative operation control strategy, including:

[0031] Initialize the population and number of iterations for the optimization algorithm;

[0032] During the iteration process, the weight factor and convergence factor are adaptively updated according to the current iteration number. Based on the updated parameters, the position of the optimized individuals in the population is updated through a shrinking and wrapping mechanism or a spiral ascent mechanism.

[0033] After the position is updated, a perturbation based on a chaotic mapping is applied to some individuals;

[0034] Evaluate the individual fitness after position update and chaotic perturbation, and update the global optimal solution;

[0035] Repeat the above iterative process until the maximum number of iterations is reached or the convergence condition is met, and output the global optimal solution obtained at this time as the optimal multi-level resource collaborative operation control strategy.

[0036] In conjunction with the first aspect, optionally, applying a perturbation based on a chaotic mapping to some individuals includes:

[0037] Chaotic sequences are generated using the Logistic chaotic mapping.

[0038] The position of the optimized individual is updated based on the chaotic sequence.

[0039] Secondly, the present invention provides a multi-level resource collaborative operation control device, comprising:

[0040] Acquisition module: Used to acquire operating data of photovoltaic, wind power, energy storage systems, electric vehicles and coal-fired units in the active distribution network during the scheduling period;

[0041] Mathematical model building module: used to build mathematical models for multi-level resources based on the acquired operational data;

[0042] Control Model Building Module: Used to construct a two-level optimization control model based on the established mathematical model, with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance exponent as the lower-level objective;

[0043] The optimization module employs an adaptive optimization algorithm incorporating adaptive weights and chaotic perturbation mechanisms to solve the constructed two-layer optimization control model, outputting the optimal multi-level resource cooperative operation control strategy.

[0044] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, wherein when the computer program / instruction is executed by a processor, it implements the steps of the multi-level resource collaborative operation control method described in the first aspect.

[0045] Fourthly, the present invention provides a computer device, comprising:

[0046] Memory, used to store computer programs / instructions;

[0047] A processor for executing the computer program / instructions to implement the steps of the multi-level resource collaborative operation control method described in the first aspect.

[0048] Fifthly, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the multi-level resource collaborative operation control method described in the first aspect.

[0049] Compared with the prior art, the beneficial effects achieved by the multi-level resource collaborative operation control method, device, equipment, medium, and program product provided in the embodiments of the present invention include:

[0050] This invention acquires operational data of photovoltaic, wind power, energy storage systems, electric vehicles, and coal-fired power units in an active distribution network during a scheduling cycle; based on the acquired operational data, mathematical models are established for each level of resources; this invention can accurately characterize the output characteristics and constraints of each resource, overcome the shortcomings of existing technology models that are simplified and do not fully consider resources, provide a reliable foundation for deep collaborative optimization of multi-level resources, and enhance the feasibility and engineering practical value of scheduling schemes;

[0051] Based on the established mathematical model, this invention constructs a two-level optimization control model with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance index as the lower-level objective. This invention can effectively solve the problem that traditional single-objective optimization models cannot simultaneously consider economy and stability. While pursuing operational economy, this invention takes the system's power balance as the core optimization objective, which can fundamentally reduce the risk of supply and demand imbalance caused by renewable energy fluctuations and load changes, and improve the power supply reliability and operational robustness of the active distribution network.

[0052] This invention employs an adaptive optimization algorithm incorporating adaptive weights and chaotic perturbation mechanisms to solve a constructed two-layer optimization control model, outputting the optimal multi-level resource collaborative operation control strategy. The algorithm provides a balance between global exploration and local exploitation capabilities through adaptive parameter updates, and effectively avoids getting trapped in local optima using chaotic perturbations, outperforming traditional optimization algorithms in both convergence speed and solution quality. This invention can quickly and reliably obtain globally optimal or near-optimal multi-level resource collaborative operation control strategies, greatly improving the optimization efficiency and decision-making level of power distribution networks.

[0053] This invention incorporates the costs of curtailed solar and wind power into the optimization objectives and provides orderly guidance for electric vehicle charging behavior. This invention can effectively promote the local consumption of fluctuating renewable energy sources such as photovoltaic and wind power at the distribution network level, reducing energy waste. At the same time, by coordinating the output of coal-fired units and clean resources, this invention reduces dependence on traditional fossil fuels and total operating costs while ensuring power supply security, thus achieving a balance between economic and environmental benefits. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a multi-level resource collaborative operation control method provided in Embodiment 1 of the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of a two-layer optimization control model in a multi-level resource collaborative operation control method provided in Embodiment 1 of the present invention;

[0056] Figure 3 This is a schematic diagram of the process of solving the constructed two-layer optimization control model using an adaptive optimization algorithm in a multi-level resource collaborative operation control method provided in Embodiment 1 of the present invention. Detailed Implementation

[0057] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0058] Example 1:

[0059] like Figure 1 As shown, this embodiment provides a multi-level resource collaborative operation control method, including:

[0060] Acquire operational data of photovoltaic, wind power, energy storage systems, electric vehicles, and coal-fired power units in the active distribution network during the scheduling period;

[0061] Based on the acquired operational data, mathematical models are established for each level of resources;

[0062] Based on the established mathematical model, a two-level optimization control model is constructed with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance index as the lower-level objective.

[0063] An adaptive optimization algorithm incorporating adaptive weights and chaotic perturbation mechanisms is employed to solve the constructed two-level optimization control model, outputting the optimal multi-level resource collaborative operation control strategy.

[0064] The specific steps include:

[0065] Step 1: Obtain the operating data of photovoltaic, wind power, energy storage systems, electric vehicles and coal-fired units in the active distribution network during the scheduling period.

[0066] Specifically, the operational data includes, but is not limited to: scheduling period T (e.g., the next 24 hours, divided into 24 time periods with 1-hour intervals); solar irradiance prediction sequence, photovoltaic array area, and photoelectric conversion efficiency at the location of the photovoltaic array; wind speed prediction sequence for wind farms, wind turbine cut-in / rated / cut-out wind speeds, and rated power; rated capacity, initial state of charge (SOC), upper and lower limits of charge and discharge power, upper and lower limits of SOC, and charge and discharge efficiency of energy storage systems; access time, departure time, initial SOC, target SOC, battery capacity, and maximum charging power of electric vehicle clusters; cost coefficient, upper and lower limits of output, and ramp rate limits of coal-fired power units; and the system's base load prediction curve.

[0067] Step 2: Based on the acquired operational data, establish mathematical models for each level of resource.

[0068] The mathematical model for photovoltaics calculates photovoltaic output based on solar radiation intensity and photoelectric conversion efficiency, and is expressed by the following formula:

[0069] (1)

[0070] In equation (1), for Photovoltaic power output at all times for At any given time, solar irradiance For the area of ​​the photovoltaic array, For photoelectric conversion efficiency, For illumination time; when When the value approaches 0, a real-time smooth output curve is fitted.

[0071] The mathematical model of wind power is a piecewise function relating the output power of a wind turbine to wind speed, expressed by the following formula:

[0072] (2),

[0073] In equation (2), Wind speed Wind power output at that time This refers to the actual wind speed. To cut into wind speed, Rated wind speed, To cut off the wind speed, This refers to the rated power of the fan.

[0074] The mathematical model of the energy storage system includes the dynamic equation of the state of charge, which satisfies the charging and discharging power constraints and the state of charge constraints.

[0075] The dynamic equation for the charged state is expressed by the following equation:

[0076] (3)

[0077] (4)

[0078] In equations (3) and (4), for The SOC value of the energy storage system after charging is constant. for The SOC value of the energy storage system after constant discharge. for The SOC value of the energy storage system at all times. For time step, For the rated capacity of the energy storage system, for The charging / discharging power of the energy storage system at all times. To improve the charging efficiency of energy storage systems, These data are used together to simulate and analyze the operating characteristics of energy storage systems, which are used to measure the discharge efficiency of the energy storage system.

[0079] To ensure the safe and stable operation of energy storage systems and extend their service life, it is essential to strictly adhere to the constraints on charging and discharging power and state of charge.

[0080] The charge / discharge power constraint is expressed by the following formula:

[0081] (5)

[0082] Equation (5), when A positive value indicates charging, and a negative value indicates discharging; This refers to the rated power of the energy storage system.

[0083] The state-of-charge constraint is expressed by the following formula:

[0084] (6)

[0085] In equation (6), Minimum SOC limit, This is the maximum SOC limit.

[0086] To avoid shortening the lifespan of energy storage due to frequent charging and discharging, the rate of change of charging and discharging power needs to be limited.

[0087] (7)

[0088] In equation (7), Due to power change rate limitation, for The charging and discharging power of the energy storage system at all times.

[0089] The mathematical models of electric vehicles include disordered charging load models and ordered charging load models.

[0090] Disorderly charging refers to the practice where users connect their electric vehicles to a charging station and immediately charge them at maximum power until the battery is fully charged. The charging power of disorderly charging... It can be expressed by the following formula:

[0091] (8)

[0092] In equation (8), For electric vehicles, For the first The maximum charging power of an electric vehicle For the first The charging start time for an electric vehicle. For the first The charging completion time of an electric vehicle;

[0093] Total disordered charging load of N electric vehicles in the system It can be expressed by the following formula:

[0094] , (9).

[0095] Orderly charging refers to adjusting the charging start time and charging power through optimization algorithms to avoid peak grid periods while meeting user charging needs. The mathematical model for orderly charging must satisfy the following constraints:

[0096] (10)

[0097] In equation (10), For the first The initial state of charge of an electric vehicle. For the first The charging start time for an electric vehicle. For the first The charging completion time of an electric vehicle; For the first electric vehicles in time Orderly charging power, For electric vehicle charging efficiency, For the first The battery capacity of an electric vehicle The target state of charge;

[0098] Total ordered charging load of N electric vehicles in the system It can be expressed by the following formula:

[0099] , (11).

[0100] The mathematical model for a coal-fired power unit is a quadratic function of the power generation cost, expressed by the following formula:

[0101] (12)

[0102] In equation (12), for The cost of generating electricity at any given moment; for Output power at time t; a, b, c are cost coefficients.

[0103] This embodiment can accurately characterize the output characteristics and constraints of each resource, and overcomes the shortcomings of existing technical models that are simplified and do not fully consider resources. It provides a reliable foundation for deep collaborative optimization of multi-level resources and enhances the feasibility and engineering practical value of scheduling schemes.

[0104] Step 3: Based on the established mathematical model, construct a two-level optimization control model with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance index as the lower-level objective;

[0105] like Figure 2 As shown, the upper-level objective function is to minimize the dispatch cost of the distribution network. After considering the costs and constraints of resources at all levels, the output strategy of each resource level is output. Then, the optimized load curve and output curve are passed to the lower-level function so that the lower-level model can calculate the power balance. Finally, after algorithm optimization, the optimal multi-level resource collaborative operation strategy is output.

[0106] The upper-level model aims to minimize the total operating cost of the system within a scheduling period T. The objective is expressed by the following formula:

[0107] (13)

[0108] (14)

[0109] (15)

[0110] In equations (13), (14), and (15), The operating cost of photovoltaic power generation includes basic operation and maintenance costs and curtailment costs. For basic operation and maintenance costs of photovoltaic systems, For abandoned light power, This is the light rejection factor; The operating cost of wind power generation includes basic operation and maintenance costs and wind curtailment costs. For basic operation and maintenance costs of wind power, For wind curtailment power, This is the wind curtailment coefficient. For the operating costs of energy storage systems, For the cost of power generation by coal-fired units, The cost of charging electric vehicles;

[0111] The lower-level model aims to minimize the power imbalance index and calculates the power imbalance degree index. It can be expressed by the following formula:

[0112] (16)

[0113] In equation (16), T The total number of time periods in the scheduling cycle. for t Total power generation of the system at any time for t Total system load power at any given time.

[0114] The two-level optimization control model also includes constraints, which include:

[0115] Power balance constraints are used to ensure that the total generating capacity of the distribution network matches the total load capacity.

[0116] The ramp-up power constraint for coal-fired power units is used to limit the rate of change of output of coal-fired power units in adjacent time periods;

[0117] The upper and lower limits of output for each power source are used to restrict the output range of photovoltaic, wind power, energy storage systems and coal-fired units.

[0118] Specifically, the power balance constraint requires that the output power of each unit in the distribution network must match the load to maintain a balance between supply and demand. The load includes the charging load of electric vehicles and the base load. If there is a deviation between the two, it will lead to power surplus or shortage, which is not conducive to the stable operation of the distribution network. This can be expressed by the following formula:

[0119] (17)

[0120] In equation (17), This is the basic electrical load power;

[0121] Specifically, the ramp-up power constraint for coal-fired power units is designed to prevent overload phenomena that can occur when the load suddenly increases or the power output is rapidly increased. To avoid this affecting the operational stability of the coal-fired power unit, a corresponding ramp-up power constraint is set, expressed by the following formula:

[0122] (18)

[0123] In equation (18), and These are the uphill and downhill ramp speeds for coal-fired power units, respectively.

[0124] Specifically, the upper and lower limits of output for each power source are constraints that need to be set when the power source is connected to the distribution network and participates in optimized dispatching to prevent it from exceeding the limits. This is expressed by the following formula:

[0125] , (19)

[0126] (20)

[0127] ,(twenty one),

[0128] ,(twenty two),

[0129] In equation (19), and These correspond to the minimum and maximum limits of photovoltaic power output, respectively.

[0130] In equation (20), and These correspond to the minimum and maximum limits of wind power output, respectively.

[0131] In equation (21), and These represent the upper and lower limits of energy storage output, respectively.

[0132] In equation (22), and These represent the upper and lower limits of the output of the coal-fired power unit, respectively.

[0133] This embodiment can effectively solve the problem that traditional single-objective optimization models cannot take into account both economy and stability. While pursuing operational economy, it takes the power balance of the system as the core optimization objective, which can fundamentally reduce the risk of supply and demand imbalance caused by renewable energy fluctuations and load changes, and improve the power supply reliability and operational robustness of the active distribution network.

[0134] Step 4: Using an adaptive optimization algorithm that incorporates adaptive weights and chaotic perturbation mechanisms, the constructed two-layer optimization control model is solved, and the optimal multi-level resource collaborative operation control strategy is output.

[0135] like Figure 3 As shown, an adaptive optimization algorithm incorporating adaptive weights and a chaotic perturbation mechanism is used to solve the constructed two-level optimization control model, including:

[0136] Step 4.1: Initialize the population and number of iterations for the optimization algorithm.

[0137] Initialize population size to Randomly generated within the search space An initial optimization individual is selected, its fitness is evaluated, and an initial optimal solution is determined. .

[0138] Step 4.2: During the iteration process, the weight factor and convergence factor are adaptively updated according to the current iteration number. Based on the updated parameters, the position of the optimized individuals in the population is updated through a shrinking and wrapping mechanism or a spiral ascent mechanism.

[0139] Step 4.2.1: Set iteration labels Initialize the number of iterations .

[0140] Step 4.2.2: Update adaptive weights and adaptive parameters Specifically:

[0141] ,(twenty three),

[0142] ,(twenty four),

[0143] In equations (23) and (24), This represents the current iteration number. The maximum number of iterations, The range of values ​​decreases linearly from 2 to 0. It is a weight that increases linearly with iteration, with a value range from 0.4 to 0.9.

[0144] Step 4.2.3: In the initial optimization phase, assuming the current best candidate solution is the objective value or close to the optimal value, this phase is represented by the following equation:

[0145] (25)

[0146] ,(26)

[0147] In equations (25) and (26), and For the coefficient vector, To optimize the distance between individuals and those they attract, This is the position vector of the currently obtained optimal solution. Given the current position vector, if a better solution is found, it should be updated in each iteration. .

[0148] vector and The calculation process is as follows:

[0149] ,(27)

[0150] (28)

[0151] In equations (27) and (28), It is a random value between [0,1].

[0152] Step 4.2.4: Assume that during the optimization process, there is a 50% probability that either a shrinking wrapping mechanism or a spiral model can be chosen to update the position of the optimized individual. The mathematical model is as follows:

[0153] ,(29)

[0154] In equation (29), It is the constant that defines the shape of the logarithmic spiral. It is Random numbers between yes A random number between [a certain number of points].

[0155] Step 4.2.5: Generate a random number with an absolute value greater than 1 or less than -1. Based on this, a repulsive force is applied to optimize individuals, prompting the individual principle to attract individuals:

[0156] (30)

[0157] (31)

[0158] In equations (30) and (31), This is the position vector of the random search solution.

[0159] Step 4.2.6: Check whether the obtained solution vector is within the search space. If not, perform boundary processing.

[0160] Step 4.2.7: Evaluate the fitness of the new coordinates. If superior, update the individual position and simultaneously update the global optimum. .

[0161] Step 4.3: After the position is updated, apply a perturbation based on a chaotic mapping to some individuals.

[0162] Step 4.3.1: Generate a chaotic sequence using the Logistic chaotic mapping. The generated sequence with chaotic properties is as follows:

[0163] , (32)

[0164] In equation (32), These are control parameters. It is the chaotic value of the s-th iteration, and the initial value. .

[0165] Step 4.3.2: Update the position of the optimized individual based on the chaotic sequence, expressed by the following formula:

[0166] (33)

[0167] In equation (33), The position after the disturbance. and These are the upper and lower bounds of the search space, respectively.

[0168] Step 4.4: Evaluate the individual fitness after position update and chaotic perturbation, and update the global optimal solution.

[0169] Calculate the fitness after the perturbation; update it if it is better, and also update the global optimal solution after the perturbation. .

[0170] Step 4.5: Repeat the above iterative process until the maximum number of iterations is reached or the convergence condition is met, and output the global optimal solution obtained at this time as the optimal multi-level resource collaborative operation control strategy.

[0171] The algorithm provided in this embodiment balances global exploration and local development capabilities through adaptive parameter updates, and effectively avoids getting trapped in local optima by utilizing chaotic perturbations. It outperforms traditional optimization algorithms in both convergence speed and solution quality.

[0172] This embodiment can quickly and reliably obtain the globally optimal or near-optimal multi-level resource collaborative operation control strategy, which greatly improves the optimization operation efficiency and decision-making level of the distribution network.

[0173] In summary, this embodiment, by incorporating the costs of curtailed solar and wind power into the optimization objectives and providing orderly guidance for electric vehicle charging behavior, can effectively promote the local consumption of fluctuating renewable energy sources such as photovoltaic and wind power at the distribution network level, thereby reducing energy waste.

[0174] Meanwhile, this embodiment, by coordinating the output of coal-fired units and clean resources, reduces dependence on traditional fossil energy and total operating costs while ensuring power supply security, thus achieving a balance between economic and environmental benefits.

[0175] Example 2:

[0176] This embodiment provides a multi-level resource collaborative operation control device, including:

[0177] Acquisition module: Used to acquire operating data of photovoltaic, wind power, energy storage systems, electric vehicles and coal-fired units in the active distribution network during the scheduling period;

[0178] Mathematical model building module: used to build mathematical models for multi-level resources based on the acquired operational data;

[0179] Control Model Building Module: Used to construct a two-level optimization control model based on the established mathematical model, with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance exponent as the lower-level objective;

[0180] Optimization module: Used to solve the constructed two-level optimization control model by adopting an adaptive optimization algorithm that incorporates adaptive weights and chaotic perturbation mechanism, and output the optimal multi-level resource cooperative operation control strategy.

[0181] Example 3:

[0182] This embodiment provides a computer-readable storage medium storing a computer program / instruction thereon. When the computer program / instruction is executed by a processor, it implements the steps of the multi-level resource collaborative operation control method described in Embodiment 1.

[0183] Example 4:

[0184] This embodiment provides a computer device, including:

[0185] Memory, used to store computer programs / instructions;

[0186] A processor is used to execute the computer program / instructions to implement the steps of the multi-level resource collaborative operation control method described in Embodiment 1.

[0187] Example 5:

[0188] This embodiment provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the multi-level resource collaborative operation control method described in Embodiment 1.

[0189] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0190] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0193] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A multi-level resource collaborative operation control method, characterized in that, include: Acquire operational data of photovoltaic, wind power, energy storage systems, electric vehicles, and coal-fired power units in the active distribution network during the scheduling period; Based on the acquired operational data, mathematical models are established for each level of resources; Based on the established mathematical model, a two-level optimization control model is constructed with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance index as the lower-level objective. An adaptive optimization algorithm incorporating adaptive weights and chaotic perturbation mechanisms is employed to solve the constructed two-level optimization control model, outputting the optimal multi-level resource collaborative operation control strategy.

2. The multi-level resource collaborative operation control method according to claim 1, characterized in that, Based on the acquired operational data, mathematical models are established for each of the multi-level resources, including: The mathematical model for photovoltaics is based on the calculation of photovoltaic output based on solar radiation intensity and photoelectric conversion efficiency; The mathematical model of wind power is a piecewise functional relationship between the output power of the wind turbine and the wind speed. The mathematical model of the energy storage system includes a dynamic equation of the state of charge, which satisfies the charging and discharging power constraints and the state of charge constraints. The mathematical model of the electric vehicle includes a disordered charging load model and an ordered charging load model; The mathematical model of the coal-fired power unit is a quadratic function of the power generation cost.

3. The multi-level resource collaborative operation control method according to claim 1, characterized in that, Based on the established mathematical model, a two-level optimization control model is constructed, with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance exponent as the lower-level objective, including: The upper-level model aims to minimize the system's performance within a scheduling cycle. T Total operating costs The objective is expressed by the following formula: ， ， ， in, The operating cost of photovoltaic power generation includes basic operation and maintenance costs and curtailment costs. For basic operation and maintenance costs of photovoltaic systems, For abandoned light power, This is the light rejection factor; The operating cost of wind power generation includes basic operation and maintenance costs and wind curtailment costs. For basic operation and maintenance costs of wind power, For wind curtailment power, This is the wind curtailment coefficient. For the operating costs of energy storage systems, For the cost of power generation by coal-fired units, Cost of charging electric vehicles; The lower-level model aims to minimize the power imbalance index and calculates the power imbalance degree index. It can be expressed by the following formula: ， Where T is the total number of time periods in the scheduling cycle. Let t be the total power generation of the system at time t. Let t be the total load power of the system at time t.

4. The multi-level resource collaborative operation control method according to claim 1, characterized in that, The two-level optimization control model also includes constraints, which include: Power balance constraints are used to ensure that the total generating capacity of the distribution network matches the total load capacity. The ramp-up power constraint for coal-fired power units is used to limit the rate of change of output of coal-fired power units in adjacent time periods; The upper and lower limits of output for each power source are used to restrict the output range of photovoltaic, wind power, energy storage systems and coal-fired units.

5. The multi-level resource collaborative operation control method according to claim 1, characterized in that, The adaptive optimization algorithm, which incorporates adaptive weights and chaotic perturbation mechanisms, solves the constructed two-layer optimization control model and outputs the optimal multi-level resource cooperative operation control strategy, including: Initialize the population and number of iterations for the optimization algorithm; During the iteration process, the weight factor and convergence factor are adaptively updated according to the current iteration number. Based on the updated parameters, the position of the optimized individuals in the population is updated through a shrinking and wrapping mechanism or a spiraling and ascending mechanism. After the position is updated, a perturbation based on a chaotic mapping is applied to some individuals; Evaluate the individual fitness after position update and chaotic perturbation, and update the global optimal solution; Repeat the above iterative process until the maximum number of iterations is reached or the convergence condition is met, and output the global optimal solution obtained at this time as the optimal multi-level resource collaborative operation control strategy.

6. The multi-level resource collaborative operation control method according to claim 5, characterized in that, The perturbation applied to some individuals based on chaotic mapping includes: Chaotic sequences are generated using the Logistic chaotic mapping. The position of the optimized individual is updated based on the chaotic sequence.

7. A multi-level resource collaborative operation control device, characterized in that, include: Acquisition module: Used to acquire operating data of photovoltaic, wind power, energy storage systems, electric vehicles and coal-fired units in the active distribution network during the scheduling period; Mathematical model building module: used to build mathematical models for multi-level resources based on the acquired operational data; Control Model Building Module: Used to construct a two-level optimization control model based on the established mathematical model, with minimizing the total system operating cost as the upper-level objective and minimizing the power imbalance exponent as the lower-level objective; Optimization module: Used to solve the constructed two-level optimization control model by adopting an adaptive optimization algorithm that incorporates adaptive weights and chaotic perturbation mechanism, and output the optimal multi-level resource cooperative operation control strategy.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the multi-level resource collaborative operation control method as described in any one of claims 1-6.

9. A computer device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the multi-level resource collaborative operation control method according to any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the multi-level resource collaborative operation control method as described in any one of claims 1-6.

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