Power consumption scene-based comprehensive benefit calculation method and system for power distribution system

Abstract

The invention relates to the technical field of intelligent power grid optimization, and discloses a power consumption scene-based power distribution system comprehensive benefit calculation method and system, and the method comprises the steps: collecting power consumption load data in a factory power distribution system; calculating a power stability factor; calculating an energy-saving potential score of the load; the load energy-saving potential score and the electricity price fluctuation are combined, and a multi-target optimization method is adopted to optimize the load adjustment amplitude; and calculating a load comprehensive benefit score. In the prior art, only a single target is concerned, such as energy saving or electric charge saving, and comprehensive constraint optimization of a plurality of targets is lacked, so that the technical problem that accurate scheduling and benefit optimization cannot be realized in actual scenes of electricity price fluctuation and load change is solved. Due to the fact that the multi-objective optimization method, the particle swarm optimization algorithm and the refined load fluctuation analysis are introduced, multi-objective comprehensive optimization is achieved, and the economic benefit, the energy-saving effect and the operation stability of the power distribution system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of distribution cabinets, and particularly relates to a method and system for calculating the comprehensive benefits of a distribution system based on electricity consumption scenarios. Background Art

[0002] Currently, traditional distribution system optimization methods mainly rely on single-objective optimization, such as energy conservation, reducing electricity bills, or load balancing. These methods often show obvious deficiencies in the face of large load fluctuations in factories, dynamic electricity price changes, and diverse electricity consumption scenarios. For example, most existing technologies are based on static load models or fixed electricity price assumptions, and in scenarios with large electricity price changes and frequent electricity demand fluctuations, they cannot accurately reflect the economic benefits and stability of actual load dispatching. Traditional methods usually cannot optimize multiple objectives simultaneously and lack a real-time feedback and adjustment mechanism, resulting in poor adaptability when dealing with electricity price fluctuations and load changes. For example, traditional methods usually only focus on energy conservation or electricity bill savings and are difficult to balance multiple objectives (such as energy conservation, load stability, and electricity bill savings) in the same optimization framework. In the case of large electricity price fluctuations at different times, existing methods cannot adjust the load in real time, resulting in the failure to effectively reduce the load during high electricity price periods and missing the opportunity to save electricity bills. In addition, existing methods often ignore the impact of load fluctuations on system stability, and when the load fluctuates greatly, it will cause equipment overload and even affect the stability of power supply. Therefore, there is an urgent need for a distribution system optimization method that can simultaneously optimize multiple objectives (such as energy conservation, load stability, and electricity bill savings) and can dynamically respond to electricity price fluctuations and load changes. This method should be able to achieve precise load dispatching and multi-objective comprehensive optimization in complex and uncertain electricity consumption scenarios to improve the economic benefits, stability, and energy conservation effect of the distribution system. Summary of the Invention

[0003] Aiming at the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for calculating the comprehensive benefits of a distribution system based on electricity consumption scenarios, aiming to solve the technical problem that existing technologies often only focus on a single objective, such as energy conservation or electricity bill savings, lack of comprehensive constraint optimization of multiple objectives, and cannot achieve precise dispatching and benefit optimization in actual scenarios of electricity price fluctuations and load changes.

[0004] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for calculating the comprehensive benefits of a distribution system based on electricity consumption scenarios,

[0005] The method for calculating the comprehensive benefits of a distribution system based on electricity consumption scenarios includes:

[0006] Step S10: Collect the electricity load data of the factory power distribution system under the electricity consumption scenario, perform multi-time scale structured processing on the electricity load data to obtain a multi-level load curve, and construct a load time series matrix based on the multi-level load curve;

[0007] Step S20: Extract the load volatility characteristics from the load time series matrix, and calculate the power stability factor based on the load volatility characteristics;

[0008] Step S30: Introduce an energy-saving adjustment coefficient, and calculate the load energy-saving potential score based on the power stability factor and the energy-saving adjustment coefficient;

[0009] Step S40: Obtain the electricity prices at different time periods, and determine the load adjustment amplitude using the multi-objective optimization method based on the load energy-saving potential score and the electricity prices at different time periods;

[0010] Step S50: Calculate the comprehensive benefit score of the load by comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment amplitude, and output the optimal scheduling plan according to the comprehensive benefit score of the load.

[0011] Preferably, in step S10, the steps of collecting the electricity load data of the factory power distribution system under the electricity consumption scenario, performing multi-time scale structured processing on the electricity load data, and constructing a load time series matrix specifically include:

[0012] Step S101: Collect the electricity load data of the factory power distribution system under the electricity consumption scenario, and generate an original data matrix based on the electricity load data; among them, the electricity load data includes equipment electricity consumption data and electricity consumption time marks; the equipment electricity consumption data includes power equipment electricity consumption data, lighting equipment electricity consumption data, and temperature control equipment electricity consumption data; the electricity consumption time marks include the start and stop time data, load value data, and operating status data of the equipment;

[0013] Step S102: Perform multi-time scale structured processing on the original data matrix to form a multi-level load curve, including a short-term fluctuation load curve, a daily periodic load curve, and a long-term trend load curve;

[0014] Step S103: Extract multi-scale load characteristics using the wavelet decomposition method according to the multi-level load curve, and establish a preliminary load time series matrix based on the multi-scale load characteristics;

[0015] Step S104: Perform data normalization and load characteristic extraction on the preliminary load time series matrix to obtain a load time series matrix including load volatility characteristics, average load level, and maximum load value.

[0016] Preferably, in step S20, the calculation formula of the power stability factor is: ;

[0017] Among them, is the power stability factor of the i-th type of load, is the average power of the i-th type of load, determined according to the load volatility characteristics; is the peak power of the i-th type of load, determined according to the load volatility characteristics; is the standard deviation of the i-th type of load, determined according to the load volatility characteristics; is the anti-zero constant.

[0018] Preferably, in step S40, the steps of obtaining the electricity prices at different time periods and determining the load adjustment range by using the multi-objective optimization method according to the load energy-saving potential score and the electricity prices at different time periods specifically include:

[0019] Step S401: Obtain the electricity prices at different time periods, and set the main objective function and objective constraint conditions, where the main objective function includes the objective function of maximizing the electricity cost savings, and the objective constraint conditions include the electricity cost savings constraint, the load stability constraint, and the energy-saving amount constraint;

[0020] Step S402: Set the constraint weight coefficients for the objective constraint conditions, and use the particle swarm optimization method to solve the main objective function to obtain the load optimization result;

[0021] Step S403: Apply the load optimization result, and further adjust the constraint weight coefficients of the objective constraint conditions according to the feedback of the load optimization result to finally determine the load adjustment range.

[0022] Preferably, in step S40, the steps of obtaining the electricity prices at different time periods and determining the load adjustment range by using the multi-objective optimization method according to the load energy-saving potential score and the electricity prices at different time periods specifically include:

[0023] Step S401: Obtain the electricity prices at different time periods, and set the main objective function and objective constraint conditions, where the main objective function includes the objective function of maximizing the electricity cost savings, and the objective constraint conditions include the electricity cost savings constraint, the load stability constraint, and the energy-saving amount constraint;

[0024] Step S402: Set the constraint weight coefficients for the objective constraint conditions, and use the particle swarm optimization method to solve the main objective function to obtain the load optimization result;

[0025] Step S403: Apply the load optimization result, and further adjust the constraint weight coefficients of the objective constraint conditions according to the feedback of the load optimization result to finally determine the load adjustment range.

[0026] Preferably, in step S40, the step of using the particle swarm optimization method to solve the main objective function to obtain the load optimization result adopts the formula: ; ;

[0027] wherein, is the velocity of particle j in the k-th generation, used to represent the change speed of the load scheduling scheme; is the position of particle j in the k-th generation, used to represent the specific content of the load scheduling scheme; is the historical optimal position of particle j; t is the global optimal position of the population, used to represent the global optimal position of the population; is the inertia weight, used to control the search range of the particle; are acceleration constants, used to control the attraction of the particle to the individual and global optimal positions; is a random number, used to simulate uncertainty.

[0028] Preferably, in step S50, the steps of comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment range to calculate the load comprehensive benefit score and outputting the optimal scheduling scheme according to the load comprehensive benefit score specifically include: comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment range, and calculating the load comprehensive benefit score of each type of load by using the weighted sum method; performing priority sorting on different loads according to the load comprehensive benefit scores of each type of load, and outputting the load scheduling scheme.

[0029] The present invention also provides a comprehensive benefit calculation system for a distribution system based on an electricity consumption scenario, including:

[0030] An electricity load data acquisition module, configured to acquire electricity load data in an electricity consumption scenario in a factory distribution system, perform multi-time scale structured processing on the electricity load data to obtain a multi-level load curve, and construct a load time series matrix according to the multi-level load curve;

[0031] A power stability factor calculation module, configured to extract load volatility characteristics from the load time series matrix and calculate the power stability factor according to the load volatility characteristics;

[0032] A load energy-saving potential calculation module, configured to introduce an energy-saving adjustment coefficient and calculate the load energy-saving potential score according to the power stability factor and the energy-saving adjustment coefficient;

[0033] A multi-objective optimization module, configured to obtain electricity prices at different time periods, and determine the load adjustment range by using the multi-objective optimization method according to the load energy-saving potential score and the electricity prices at different time periods;

[0034] A comprehensive benefit score calculation module, configured to comprehensively use the power stability factor, the load energy-saving potential score, and the load adjustment range to calculate the load comprehensive benefit score, and output the optimal scheduling scheme according to the load comprehensive benefit score.

[0035] The present invention also provides a computer program product, including a comprehensive benefit calculation program for a power distribution system based on an electricity consumption scenario. When the comprehensive benefit calculation program for the power distribution system based on the electricity consumption scenario is executed by a processor, the comprehensive benefit calculation method for the power distribution system based on the electricity consumption scenario as described above is implemented.

[0036] The beneficial effects of the present invention are as follows: Compared with the prior art that often only focuses on a single objective, such as energy conservation or electricity cost savings, and lacks comprehensive constraint optimization for multiple objectives, resulting in the technical problem that accurate scheduling and benefit optimization cannot be achieved in the actual scenarios of electricity price fluctuations and load changes; since the present application introduces a multi-objective optimization method, a particle swarm optimization algorithm, and a refined load fluctuation analysis, multi-objective comprehensive optimization is achieved, improving the economic benefits, energy conservation effect, and operation stability of the power distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a schematic flowchart of the first embodiment of a comprehensive benefit calculation method for a power distribution system based on an electricity consumption scenario of the present invention.

[0039] Figure 2 It is a schematic diagram of the device of a comprehensive benefit calculation method for a power distribution system based on an electricity consumption scenario of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0041] Embodiment 1: As Figure 1 shown, it is a schematic flowchart of the first embodiment of the comprehensive benefit calculation method for a power distribution system based on an electricity consumption scenario of the present invention, and the first embodiment of the comprehensive benefit calculation method for a power distribution system based on an electricity consumption scenario of the present invention is proposed.

[0042] In the first embodiment, the comprehensive benefit calculation method for the power distribution system based on the electricity consumption scenario includes:

[0043] Step S10: Collect the electricity load data of the factory power distribution system under the electricity consumption scenario, perform multi-time scale structured processing on the electricity load data to obtain a multi-level load curve, and construct a load time series matrix based on the multi-level load curve;

[0044] It should be noted that in step S10, the steps of collecting the electricity load data of the factory power distribution system under the electricity consumption scenario, performing multi-time scale structured processing on the electricity load data, and constructing a load time series matrix specifically include:

[0045] Step S101: Collect the electricity load data of the factory power distribution system under the electricity consumption scenario, and generate an original data matrix based on the electricity load data; among them, the electricity load data includes equipment electricity consumption data and electricity consumption time marks; the equipment electricity consumption data includes power equipment electricity consumption data, lighting equipment electricity consumption data, and temperature control equipment electricity consumption data; the electricity consumption time marks include the start and stop time data, load value data, and operating status data of the equipment;

[0046] Step S102: Perform multi-time scale structured processing on the original data matrix to form a multi-level load curve, including a short-term fluctuation load curve, a daily periodic load curve, and a long-term trend load curve;

[0047] Step S103: Use the wavelet decomposition method to extract multi-scale load characteristics according to the multi-level load curve, and establish a preliminary load time series matrix based on the multi-scale load characteristics;

[0048] Step S104: Perform data normalization and load characteristic extraction on the preliminary load time series matrix to obtain a load time series matrix including load volatility characteristics, average load level, and maximum load value.

[0049] It should be understood that the original data matrix is a two-dimensional matrix structure formed by the combination of equipment electricity consumption data and time marks. Each row represents the equipment, and the columns represent the electricity consumption data and time marks of the equipment at each moment. Through these data matrices, the time series data of load changes can be established.

[0050] For example, assume that the electricity consumption data of the power equipment in a certain factory is: 50 kW at 8:00, 60 kW at 9:00, and 55 kW at 10:00. These data are recorded through the corresponding time marks and form an original data matrix. The combination of load data and time marks can provide time series data for subsequent analysis.

[0051] Step S20: Extract the load volatility characteristics from the load time series matrix, and calculate the power stability factor according to the load volatility characteristics;

[0052] It should be noted that in step S20, the calculation formula for the power stability factor is as follows: ;

[0053] Wherein, is the power stability factor of the i-th type of load, is the average power of the i-th type of load, which is determined according to the load volatility characteristics; is the peak power of the i-th type of load, which is determined according to the load volatility characteristics; is the standard deviation of the i-th type of load, which is determined according to the load volatility characteristics; is the anti-zero constant.

[0054] It can be understood that the power stability factor (PSI) combines the average power, peak power, and volatility (measured by the standard deviation) of the load. If the load volatility is large and the standard deviation is high, the power stability factor will be low, indicating that the load has a greater impact on the stability of the distribution system. On the contrary, the power stability factor of a load with smaller volatility is higher, and its impact on the distribution system is smaller.

[0055] It should be understood that this formula reflects the stability of the load, and stability is a crucial factor in power system optimization. The stability of the load directly affects the service life of power equipment and the operating efficiency of the power system. Loads with smaller load volatility are more reliable in scheduling during peak hours and are also more conducive to optimizing the energy efficiency of the power grid. The calculation process of the standard deviation is a measure of load fluctuation, while the peak power and average power help evaluate the fluctuation range of the load at different times. By combining these factors, the power stability factor can quantify the degree of load fluctuation, thereby providing a basis for optimizing load scheduling.

[0056] For example, assume that the load data of the power equipment in a certain factory is as follows:

[0057] During a certain period (such as 1 hour), the load fluctuation of this equipment is: [40 kW, 60 kW, 55 kW, 50kW]. At this time: the average power is calculated as kW, the peak power is 60 kW, and the standard deviation is calculated as: kW, and the power stability factor is calculated as: , from which it can be seen that the value of the power stability factor is 0.736, indicating that this load has a certain stability, but its volatility has a greater impact, and the system may be affected by greater fluctuations.

[0058] Step S30: Introduce an energy-saving adjustment coefficient, and calculate the load energy-saving potential score based on the power stability factor and the energy-saving adjustment coefficient;

[0059] It should be noted that in Step S30, the calculation formula for the load energy-saving potential score is: ;

[0060] Among them, is the energy-saving adjustment coefficient, is the load energy-saving potential score of the i-th type of load, is the power stability factor, is the preset optimization efficiency of load scheduling.

[0061] It can be understood that the load energy-saving potential score reflects the energy-saving effect of the load during the scheduling optimization process by combining the power stability factor and the energy-saving adjustment coefficient. Loads with higher stability (i.e., larger power stability factors) usually have better energy-saving potential. Therefore, these loads can be more relied on to achieve the energy-saving goal during the optimization process. The energy-saving adjustment coefficient reflects the energy-saving effect of load scheduling. During the scheduling process, adjusting this coefficient can flexibly meet the optimization requirements of different loads.

[0062] It should be understood that the calculation of this formula depends on the stability of the load and the energy-saving adjustment coefficient of the scheduling strategy. Loads with higher stability usually have greater potential for scheduling during peak hours and can more effectively reduce energy consumption. In addition, the optimization efficiency takes into account the energy losses during the scheduling process. Therefore, the actual energy-saving effect will be slightly lower than the theoretical value. The higher the energy-saving potential score, the more significant the energy-saving effect after optimizing the load.

[0063] Step S40: Obtain the electricity prices at different time periods, and determine the load adjustment amplitude using the multi-objective optimization method based on the load energy-saving potential score and the electricity prices at different time periods;

[0064] It should be noted that in Step S40, the steps of using the particle swarm optimization method to solve the main objective function to obtain the load optimization result are as follows. The formula is: ; ;

[0065] Among them, is the velocity of particle j in the k-th generation, which is used to represent the change speed of the load scheduling plan; is the position of particle j in the k-th generation, which is used to represent the specific content of the load scheduling plan; is the historical optimal position of particle j; t is the global optimal position of the group, which is used to represent the global optimal position of the group; is the inertia weight, which is used to control the search range of the particles; are acceleration constants, which are used to control the attraction of the particles to the individual and global optimal positions; is a random number, which is used to simulate uncertainty.

[0066] It can be understood that the particle swarm optimization method explores the optimal solution by simulating the flight of particles in the solution space and continuously updating the velocity and position of each particle. By adjusting the velocity and position of the particles, we can gradually optimize the adjustment range of the load, making the load scheduling in each time period more in line with the target requirements (such as electricity cost savings, load stability, etc.). In the velocity update formula, the design of the inertia weight and acceleration constants can ensure that the particles can maintain a certain degree of exploration and can also converge to the global optimal solution faster. The random number introduces uncertainty, enabling the particles to jump out of the local optimal solution and increasing the diversity of the search.

[0067] It should be understood that through the particle swarm optimization method, we can not only consider the electricity price fluctuation and the load energy-saving potential score, but also achieve multi-objective optimization (such as energy conservation, electricity cost savings, and load stability) by dynamically adjusting the load scheduling. This process requires repeated iteration until the optimal load scheduling scheme that meets each objective is found.

[0068] For example, assume that in a factory, for the problem of optimal load scheduling of power equipment, through the particle swarm optimization method, the particle The velocity in the first generation is initialized to 0, and the initial position of the particle (load scheduling scheme) is 50 kW. During the flight process, the particle adjusts its position according to the objective function (electricity cost savings and energy-saving potential), gradually approaching the optimal solution. The inertia weight , indicating that the particle will continue to move forward along the current velocity to maintain a certain search stability. The acceleration constants , these two constants control the attraction of the particle to the historical optimal position and the global optimal position. The random number , depending on the different random numbers, the particle will approach the personal optimal position or the global optimal position with different weights. After several generations of optimization, the position of the particle will gradually approach the global optimal solution, that is, an optimized load scheduling scheme is obtained. For example, the load in a certain time period is adjusted to 45 kW to maximize energy conservation and reduce electricity costs.

[0069] Step S50: Comprehensively utilize the power stability factor, the load energy-saving potential score, and the load adjustment range to calculate the load comprehensive benefit score, and output the optimal scheduling scheme according to the load comprehensive benefit score.

[0070] It should be noted that in step S50, the steps of calculating the comprehensive load benefit score by comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment range, and outputting the optimal scheduling scheme according to the comprehensive load benefit score specifically include: comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment range, and calculating the comprehensive load benefit score of each type of load by using the weighted sum method; sorting the priorities of different loads according to the comprehensive load benefit scores of each type of load, and outputting the load scheduling scheme.

[0071] It can be understood that the weighted sum method is a commonly used method for synthesizing multiple objective functions. By setting different weight coefficients, the goals of power stability, energy conservation, and load adjustment can be balanced according to specific requirements. For example, in the case of large electricity price fluctuations, more attention may be paid to electricity cost savings, while when the system load is high, more attention may be paid to the stability of the load. Through this method, the finally obtained comprehensive load benefit score will be able to comprehensively reflect the priorities of each load under multi-objective optimization, thus providing effective guidance for subsequent load scheduling.

[0072] It should be understood that this step realizes multi-objective optimization by comprehensively considering multiple objective functions (such as electricity cost savings, load stability, and energy conservation), which is more comprehensive than single-objective optimization methods. Multi-objective optimization can ensure that while achieving energy conservation, the stability of the system is maintained, and electricity cost savings are optimized.

[0073] Embodiment 2: In addition, a comprehensive benefit calculation system for a distribution system based on an electricity consumption scenario provided by the present invention adopts a comprehensive benefit calculation method for a distribution system based on an electricity consumption scenario in the above embodiment, and can solve the technical problem of calculating the comprehensive benefit of a distribution system based on an electricity consumption scenario. Compared with the prior art, the beneficial effects of a comprehensive benefit calculation system for a distribution system based on an electricity consumption scenario provided by the present invention are the same as those of a comprehensive benefit calculation method for a distribution system based on an electricity consumption scenario provided in the above embodiment, and other technical features in the comprehensive benefit calculation system for a distribution system based on an electricity consumption scenario are the same as those disclosed in the above embodiment method, and will not be elaborated here.

[0074] Embodiment 3: The present invention provides a comprehensive benefit calculation device for a distribution system based on an electricity consumption scenario. Please refer to Figure 2, A power distribution system comprehensive benefit calculation device based on power consumption scenarios includes: 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute a power distribution system comprehensive benefit calculation method in Embodiment 1 above. The power distribution system comprehensive benefit calculation device in the embodiments of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The power distribution system comprehensive benefit calculation device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. The power distribution system comprehensive benefit calculation device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the power distribution system comprehensive benefit calculation device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the power distribution system comprehensive benefit calculation device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a power distribution system comprehensive benefit calculation device with various systems, it should be understood that it is not required to implement or have all the shown systems. Instead, more or fewer systems may be implemented or had.

[0075] Embodiment 4: The present invention also provides a computer program product, including a computer program, which when executed by a processor implements the steps of a method for calculating the comprehensive benefit of a power distribution system based on the power consumption scenario as described above. The computer program product provided by the present invention can solve the technical problem of calculating the comprehensive benefit of a power distribution system based on the power consumption scenario. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the method for calculating the comprehensive benefit of a power distribution system based on the power consumption scenario provided in the above embodiment, and will not be elaborated here.

[0076] Specifically, according to the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment disclosed by the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, it executes the above-mentioned functions defined in the methods of the embodiments disclosed by the present invention.

[0077] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0078] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A comprehensive benefit calculation method for a distribution system based on electricity consumption scenarios, characterized in that, The method includes: Step S10: Collect the electricity load data in the power consumption scenario of the factory power distribution system, perform multi-time scale structured processing on the electricity load data to obtain a multi-level load curve, and construct a load time series matrix based on the multi-level load curve; Step S20: Extract the load volatility characteristics from the load time series matrix, and calculate the power stability factor based on the load volatility characteristics; Step S30: Introduce an energy-saving adjustment coefficient, and calculate the load energy-saving potential score based on the power stability factor and the energy-saving adjustment coefficient; Step S40: Obtain the electricity prices at different times, and determine the load adjustment range by using the multi-objective optimization method according to the load energy-saving potential score and the electricity prices at different times; Step S50: Calculate the comprehensive benefit score of the load by comprehensively using the power stability factor, the load energy-saving potential score and the load adjustment range, and output the optimal scheduling plan according to the comprehensive benefit score of the load.

2. The comprehensive benefit calculation method of a power distribution system based on power consumption scenarios according to claim 1, characterized in that In step S10, the steps of collecting the electricity load data in the power consumption scenario of the factory power distribution system, performing multi-time scale structured processing on the electricity load data, and constructing a load time series matrix specifically include: Step S101: Collect the electricity load data in the power consumption scenario of the factory power distribution system, and generate an original data matrix according to the electricity load data; among them, the electricity load data includes equipment power consumption data and power consumption time marks; the equipment power consumption data includes power equipment power consumption data, lighting equipment power consumption data and temperature control equipment power consumption data; the power consumption time marks include the start and stop time data, load value data and operation status data of the equipment; Step S102: Perform multi-time scale structured processing on the original data matrix to form a multi-level load curve, including a short-term fluctuation load curve, a daily periodic load curve and a long-term trend load curve; Step S103: Extract multi-scale load characteristics by using the wavelet decomposition method according to the multi-level load curve, and establish a preliminary load time series matrix according to the multi-scale load characteristics; Step S104: Perform data normalization and load characteristic extraction on the preliminary load time series matrix to obtain a load time series matrix including load volatility characteristics, average load level and maximum load value.

3. The comprehensive benefit calculation method of a power distribution system based on electricity consumption scenarios according to claim 1, characterized in that, In step S20, the calculation formula of the power stability factor is: ； Among them, is the power stability factor of the i-th type of load, is the average power of the i-th type of load, determined according to the load volatility characteristics; is the peak power of the i-th type of load, determined according to the load volatility characteristics; is the standard deviation of the i-th type of load, determined according to the load volatility characteristics; is the anti-zero constant.

4. The integrated benefit calculation method of a power distribution system based on electricity consumption scenarios according to claim 1, characterized in that, In step S30, the calculation formula of the load energy-saving potential score is: ； Among them, is the energy-saving adjustment coefficient, is the energy-saving potential score of the i-th type of load, is the power stability factor, is the optimized efficiency of the preset load scheduling.

5. The integrated benefit calculation method of a power distribution system based on electricity consumption scenarios according to claim 1, wherein, In step S40, the steps of obtaining the electricity prices at different times and determining the load adjustment range by using the multi-objective optimization method according to the load energy-saving potential score and the electricity prices at different times specifically include: Step S401: Obtain the electricity prices at different times, set the main objective function and objective constraint conditions, where the main objective function includes the objective function of maximizing electricity cost savings, and the objective constraint conditions include electricity cost savings constraint, load stability constraint and energy saving amount constraint; Step S402: Set the constraint condition weight coefficients for the objective constraint conditions, and use the particle swarm optimization method to solve the main objective function to obtain the load optimization result; Step S403: Apply the load optimization result, and further adjust the constraint condition weight coefficients according to the feedback of the load optimization result to finally determine the load adjustment range.

6. The integrated benefit calculation method of a power distribution system based on power consumption scenarios according to claim 5, characterized in that In step S40, the step of using the particle swarm optimization method to solve the main objective function and obtain the load optimization result adopts the formula: ； ； Among them, is the velocity of particle j in the k-th generation, which is used to represent the change speed of the load scheduling scheme; is the position of particle j in the k-th generation, which is used to represent the specific content of the load scheduling scheme; is the historical optimal position of particle j; t is the global optimal position of the population, which is used to represent the global optimal position of the population; is the inertia weight, which is used to control the search range of the particle; is the acceleration constant, which is used to control the attraction of the particle to the individual and global optimal positions; is a random number, which is used to simulate uncertainty.

7. The integrated benefit calculation method of a power distribution system based on power consumption scenarios according to claim 1, characterized in that, In step S50, the step of comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment range to calculate the load comprehensive benefit score and output the optimal scheduling plan according to the load comprehensive benefit score specifically includes: comprehensively using the power stability factor, the load energy-saving potential score, and the load adjustment range, and using the weighted sum method to calculate the load comprehensive benefit score of each type of load; sorting the priorities of different loads according to the load comprehensive benefit score of each type of load, and outputting the load scheduling plan.

8. A comprehensive benefit calculation system for a distribution system based on electricity consumption scenarios, which is applied to a comprehensive benefit calculation method for a distribution system based on electricity consumption scenarios described in any one of claims 1-7, characterized in that The comprehensive benefit calculation system of the distribution system based on the power consumption scenario includes: The power consumption load data acquisition module is used to collect the power consumption load data in the power consumption scenario of the factory distribution system, perform multi-time scale structured processing on the power consumption load data to obtain a multi-level load curve, and construct a load time series matrix according to the multi-level load curve; The power stability factor calculation module is used to extract the load volatility characteristics from the load time series matrix and calculate the power stability factor according to the load volatility characteristics; The load energy-saving potential calculation module is used to introduce an energy-saving adjustment coefficient and calculate the load energy-saving potential score according to the power stability factor and the energy-saving adjustment coefficient; The multi-objective optimization module is used to obtain the electricity price at different time periods, and determine the load adjustment range by using the multi-objective optimization method according to the load energy-saving potential score and the electricity price at different time periods; The comprehensive benefit score calculation module is used to comprehensively use the power stability factor, the load energy-saving potential score, and the load adjustment range to calculate the load comprehensive benefit score, and output the optimal scheduling plan according to the load comprehensive benefit score.

9. An integrated benefit calculation device for a distribution system based on electricity consumption scenarios, characterized in that, The comprehensive benefit calculation device of the distribution system based on the power consumption scenario includes: a memory, a processor, and a comprehensive benefit calculation program of the distribution system based on the power consumption scenario stored on the memory and executable on the processor. When the comprehensive benefit calculation program of the distribution system based on the power consumption scenario is executed by the processor, it implements a comprehensive benefit calculation method of the distribution system based on the power consumption scenario according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a comprehensive benefit calculation program of the distribution system based on the power consumption scenario. When the comprehensive benefit calculation program of the distribution system based on the power consumption scenario is executed by the processor, it implements a comprehensive benefit calculation method of the distribution system based on the power consumption scenario according to any one of claims 1 to 7.

Images