Intelligent park energy consumption management method and device, electronic equipment and storage medium

Abstract

This application provides a smart park energy consumption management method and device, electronic equipment, and storage medium, belonging to the field of intelligent energy management technology. The method includes: dividing the smart park into multiple business type zones based on regional business types; encoding the energy-consuming equipment types, operating periods, and energy allocation ratios of each business type zone using chromosomes based on target energy consumption boundary constraints to generate an initial population; performing genetic operations on the initial population to obtain a target population, and using chromosomes with fitness values ​​greater than a first fitness threshold in the target population as the target solution set; constructing an initial particle swarm based on the target solution set, and iteratively updating the initial particle swarm until the iterative convergence condition is met; using the particle with the highest fitness during the iterative update process as the target particle, and managing the energy consumption of the smart park based on the parameters of the target particle. This application can improve the adaptability and parameter control accuracy of energy consumption management solutions and reduce the overall energy consumption cost of the park.

Description

Technical Field

[0001] This application belongs to the field of energy intelligent management and control technology, and more specifically, it relates to smart park energy consumption management methods and devices, electronic devices, and storage media. Background Technology

[0002] Smart parks leverage next-generation information technologies such as the Internet of Things, artificial intelligence, and cloud computing to comprehensively perceive, integrate, and intelligently schedule various resources within the park, including buildings, security systems, and operations and maintenance, achieving a modern park form that is highly efficient, energy-saving, safe, and convenient. With the expansion and diversification of park scale, the number and types of energy-consuming equipment have surged, and the energy consumption structure exhibits multi-source and fragmented characteristics, exacerbating the complexity of park energy management. Current technologies for smart park energy management mostly employ traditional zoned monitoring or single-dimensional optimization strategies, which still suffer from poor solution adaptability, difficulty in meeting the complex requirements of multi-business parks, and insufficient accuracy in optimizing energy consumption parameters, making it difficult to cope with complex optimization scenarios, leading to problems such as energy waste and cost overruns. Summary of the Invention

[0003] The purpose of this application is to provide smart park energy management methods and devices, electronic devices, and storage media to improve the adaptability of energy management solutions and the accuracy of parameter control, thereby reducing the overall energy cost of the park.

[0004] A first aspect of this application provides a smart park energy consumption management method, including:

[0005] The smart park is divided into multiple business type zones based on the regional business type; the regional business type includes industrial, office and commercial types.

[0006] Determine the target energy consumption boundary constraints for multiple business zones; based on the target energy consumption boundary constraints, perform chromosome coding on the energy-consuming equipment type, operating period, and energy allocation ratio of each business zone to generate an initial population; the chromosome includes a business zone identifier segment, equipment type coding segment, operating period coding segment, energy allocation ratio coding segment, and verification segment;

[0007] Genetic operations are performed on the initial population to obtain the target population. Chromosomes in the target population with fitness values ​​greater than the first fitness threshold are used as the target solution set.

[0008] An initial particle swarm is constructed based on the target solution set, and the initial particle swarm is iteratively updated until the iterative convergence condition is met. The particles in the constructed initial particle swarm correspond one-to-one with the chromosomes in the target solution set. The parameters included in each constructed particle are the equipment operating power of each business zone, the charging and discharging power of energy storage equipment, the renewable energy access regulation power, and the peak and valley energy consumption allocation coefficient.

[0009] The particle with the highest fitness during the iterative update process is used as the target particle, and energy consumption management of the smart park is carried out based on the parameters of the target particle.

[0010] A second aspect of this application provides a smart park energy management device, comprising:

[0011] The business type zoning module is used to divide the smart park into multiple business type zones based on the regional business type; regional business type includes industrial, office and commercial types;

[0012] The initialization module is used to determine the target energy consumption boundary constraints for multiple business zones; based on the target energy consumption boundary constraints, the energy-consuming equipment type, operating period, and energy allocation ratio of each business zone are encoded using chromosomes to generate an initial population; the chromosome includes a business zone identifier segment, an equipment type encoding segment, an operating period encoding segment, an energy allocation ratio encoding segment, and a verification segment;

[0013] The genetic module is used to perform genetic operations on the initial population to obtain the target population, and the chromosomes in the target population with fitness values ​​greater than the first fitness threshold are used as the target solution set.

[0014] The optimization parameter module is used to construct an initial particle swarm based on the target solution set and iteratively update the initial particle swarm until the iterative convergence condition is met. The particles in the constructed initial particle swarm correspond one-to-one with the chromosomes in the target solution set. The parameters included in each constructed particle are the equipment operating power of each business zone, the charging and discharging power of energy storage equipment, the renewable energy access regulation power, and the peak and valley energy consumption allocation coefficient.

[0015] The energy management module is used to manage the energy consumption of the smart park based on the parameters of the target particle, with the particle having the highest fitness during the iterative update process as the target particle.

[0016] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described smart park energy management method.

[0017] In a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described smart park energy management method.

[0018] The beneficial effects of the smart park energy consumption management method and device, electronic equipment, and storage medium provided in this application embodiment are as follows:

[0019] This application's embodiments first divide the area into zones based on the differences in business types (industrial, office, and commercial), then formulate exclusive energy consumption constraints for each zone, and bind zone identifiers with key information such as equipment type and operating time through chromosome coding. This achieves precise matching of the energy consumption characteristics of different zones, solving the problem of insufficient adaptability of traditional solutions. This application's embodiments first determine feasible basic solutions through reasonable screening, and then precisely optimize detailed parameters such as equipment operating power and energy storage scheduling. This two-step collaboration can effectively cope with complex scenarios involving multiple coupled links, ensuring that the parameters of each energy consumption link are in an optimal state, reducing energy waste from the source.

[0020] In summary, the embodiments of this application, through the design of zone adaptation and precise control, can not only ensure that the energy consumption of the park meets various constraints, but also maximize the reduction of comprehensive energy consumption costs, achieving the dual goals of efficient energy utilization and reasonable cost control, and providing a simple, feasible and effective energy management solution for smart parks. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a smart park energy consumption management method provided in an embodiment of this application;

[0023] Figure 2 This is a structural block diagram of a smart park energy management device provided in an embodiment of this application;

[0024] Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0027] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0028] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a smart park energy management method according to an embodiment of this application. The method can be executed by an electronic device, and specifically, the method can include S101 to S105.

[0029] S101: The smart park is divided into multiple business type zones according to the regional business type; the regional business type includes industrial type, office type and commercial type.

[0030] In this embodiment, the regional business type is a classification based on the functional positioning of the park area, used to differentiate the energy demand differences in different areas. The industrial type is a regional business type with manufacturing as its core function; the office type is a regional business type with daily office work as its core function; and the commercial type is a regional business type with business services as its core function. Business type zoning is a management unit formed after dividing the smart park according to the regional business type, used to achieve performance-based energy consumption management.

[0031] Considering the significant differences in energy consumption characteristics across different functional areas of a smart park, industrial areas prioritize continuous production energy consumption, office areas focus on stable energy consumption during office hours, and commercial areas are characterized by concentrated business hours and large fluctuations in energy consumption. However, existing technologies employ a unified management model, which cannot adapt to the complex energy needs of multiple business types, resulting in poor energy management effectiveness. Therefore, this embodiment divides the area into business type zones and formulates specific management strategies for the energy consumption characteristics of different zones. This lays the foundation for subsequent precise setting of energy consumption constraints and optimization of energy consumption parameters, fundamentally solving the problem of poor adaptability of traditional solutions and improving the targeting and effectiveness of energy consumption management.

[0032] For example, this embodiment can first determine basic data such as the functional layout of the smart park's areas, the uses of existing facilities, and the types of business activities, and establish a park area information archive. This embodiment can classify each area of ​​the park according to preset business type classification standards, classifying areas with manufacturing as the main activity and equipped with industrial production equipment as industrial type areas, areas with office as the main activity and equipped with office equipment as office type areas, and areas with commercial operation as the main activity and equipped with commercial service facilities as commercial type areas. This embodiment can combine factors such as the park's physical boundaries and pipeline distribution to clarify the specific scope and boundary coordinates of each business type zone. Finally, a unique identifier is assigned to each business type zone, forming a business type zone management list containing information such as zone type, scope, and identifier, providing a basis for subsequent determination of target energy consumption boundary constraints.

[0033] S102: Determine the target energy consumption boundary constraints for multiple business zones; based on the target energy consumption boundary constraints, perform chromosome coding on the energy-consuming equipment type, operating period, and energy allocation ratio of each business zone to generate an initial population; the chromosome includes a business zone identifier segment, equipment type coding segment, operating period coding segment, energy allocation ratio coding segment, and verification segment.

[0034] In this embodiment, the target energy consumption boundary constraints for multiple business type zones are determined, specifically including:

[0035] Acquire equipment data, energy consumption curves, energy consumption boundary data, and energy resource data from multiple business sectors;

[0036] For each business type zone, the energy consumption curves corresponding to that business type zone are divided into a first energy consumption curve set and a second energy consumption curve set according to the scenario identifier. The scenario identifier of the first energy consumption curve set is the weekday scenario, and the scenario identifier of the second energy consumption curve set is the holiday scenario. The first energy consumption characteristic is obtained based on the first energy consumption curve set, and the second energy consumption characteristic is obtained based on the second energy consumption curve set. The target energy consumption boundary constraint of that business type zone is determined based on the first energy consumption characteristic, the second energy consumption characteristic, equipment data, energy consumption boundary data, and energy resource data.

[0037] In this embodiment, the target energy consumption boundary constraint for the business zone is determined based on the first energy consumption characteristic, the second energy consumption characteristic, equipment data, energy consumption boundary data, and energy resource data. Specifically, this includes: determining the first energy consumption boundary constraint based on the first energy consumption characteristic, equipment data, energy consumption boundary data, and energy resource data; determining the second energy consumption boundary constraint based on the second energy consumption characteristic, equipment data, energy consumption boundary data, and energy resource data; and using the first energy consumption boundary constraint and the second energy consumption boundary constraint as the target energy consumption boundary constraint.

[0038] In this embodiment, the target energy consumption boundary constraint is a set of constraints set by the smart park based on the differences in different business formats and scenarios to ensure the bottom line of core energy consumption needs, including the range of equipment operating parameters, the upper limit of total energy consumption, and energy consumption priorities. Energy-consuming equipment type refers to the category of energy-consuming equipment in each business format zone, configured according to the functional requirements of the zone. Operating time segment is the working time interval of energy-consuming equipment, determined according to the operational needs of the business format. Energy allocation ratio is the proportion of different energy types in total energy consumption, used to balance energy utilization. Chromosome coding is the conversion of energy consumption management-related parameters into an algorithm-processable encoding format. The initial population is the first set of candidate solutions for algorithm optimization. The business format zone identifier segment is the encoding part in the chromosome that identifies the corresponding business format zone; the equipment type encoding segment is the encoding part that records the type of energy-consuming equipment; the operating time segment encoding segment is the encoding part that characterizes the operating time of the equipment; the energy allocation ratio encoding segment is the encoding part that reflects the energy allocation ratio; and the verification segment is the encoding part that verifies the validity of the chromosome.

[0039] Equipment data comprises parameter information of equipment within a business zone, including rated power and energy efficiency. Energy consumption curves reflect the change in energy consumption over time within a zone. Energy consumption boundary data includes upper and lower limits and compliance standards for energy consumption within the park or zone. Energy resource data contains information on various types of energy available within the park. Scene identifiers are identifying information that distinguishes energy consumption scenarios. The first energy consumption curve set is the set of energy consumption curves identified as weekday scenarios, and the second energy consumption curve set is the set of energy consumption curves identified as holiday scenarios. Weekday scenarios represent energy consumption scenarios corresponding to normal business operations on weekdays, while holiday scenarios represent energy consumption scenarios corresponding to statutory or agreed-upon holidays. The first energy consumption feature characterizes weekday energy consumption patterns, and the second energy consumption feature characterizes holiday energy consumption patterns. The first energy consumption boundary constraint is the energy consumption limit adapted to weekday scenarios, and the second energy consumption boundary constraint is the energy consumption limit adapted to holiday scenarios.

[0040] Considering the significant differences in energy demand between weekdays and holidays across different business sectors, a single energy consumption constraint cannot adapt to the energy consumption characteristics of different scenarios, easily leading to unreasonable constraints or energy waste. Therefore, this embodiment acquires multi-dimensional data to comprehensively grasp the basic information of energy consumption in each sector. This embodiment divides the energy consumption curve set according to scenario identifiers and extracts energy consumption characteristics, accurately capturing the energy consumption patterns of different scenarios and providing a basis for scenario-based constraints. This embodiment divides the constraints into first energy consumption boundary constraints and second energy consumption boundary constraints, making the constraints more closely aligned with actual energy consumption scenarios. This embodiment uses chromosome encoding for relevant parameters to transform abstract energy consumption management requirements into a form that the algorithm can process. The initial population provides diverse candidate solutions for subsequent genetic algorithm optimization, avoiding optimization from getting trapped in local optima and ensuring the scientific nature and effectiveness of energy consumption management optimization from the source.

[0041] For example, this embodiment can obtain equipment data for each business area through channels such as the park's energy management platform and equipment monitoring system, including the rated power, operating energy efficiency, and service life of production equipment, office equipment, and commercial facilities. This embodiment can obtain hourly energy consumption data for the past 12 months and generate energy consumption curves for each area. This embodiment can obtain energy consumption boundary data such as the park's set energy consumption limits and grid connection standards, as well as energy resource data such as photovoltaic installed capacity and wind power output characteristics.

[0042] This embodiment can determine the scene identifier based on the date attribute, classifying the energy consumption curves corresponding to Monday through Friday into a first energy consumption curve set, and the energy consumption curves corresponding to Saturday, Sunday, and statutory holidays into a second energy consumption curve set. Based on the first energy consumption curve set, this embodiment can calculate indicators such as average energy consumption on weekdays, peak-valley energy consumption ratio, and maximum load to form a first energy consumption characteristic. Based on the second energy consumption curve set, this embodiment can calculate indicators such as average energy consumption and load fluctuation amplitude on holidays to form a second energy consumption characteristic.

[0043] This embodiment can combine the first energy consumption characteristics, equipment rated power, park energy consumption upper limit, and maximum grid-connected photovoltaic and wind power capacity to determine the first energy consumption boundary constraints, such as the upper limit of peak energy consumption on weekdays and the lower limit of renewable energy grid connection ratio. This embodiment can combine the second energy consumption characteristics, equipment operation and maintenance requirements, energy consumption boundary data, and energy resource data to determine the second energy consumption boundary constraints, such as the energy consumption reduction ratio during holidays and the energy storage charging and discharging boundary. The combination of the two constitutes the target energy consumption boundary constraints.

[0044] In this embodiment, chromosomes can be constructed according to preset coding rules. Specifically, the business type partition identification segment uses 4 digits to identify the partition type and number, the equipment type coding segment uses 6 digits to record the equipment category and model, the running time coding segment uses 8 digits to represent the start and end time, the energy allocation ratio coding segment uses 6 digits to reflect the proportion of various energy sources, and the verification segment uses 3 digits to verify the validity of the coding. All the coded chromosomes are integrated to generate an initial population with suitable scale.

[0045] This embodiment extracts energy consumption characteristics by scenario-based analysis and determines target energy consumption boundary constraints, enabling the constraint rules to accurately adapt to the energy consumption patterns of different scenarios in various business sectors, thus solving the problem of poor adaptability of traditional single constraints. Chromosome encoding standardizes energy management parameters, providing a clear object for subsequent algorithm optimization, while the initial population ensures the diversity of optimization. The overall design improves the rationality of energy consumption constraints and the targeting of energy management, laying the foundation for subsequent precise optimization and effectively reducing energy waste and compliance risks.

[0046] S103: Perform genetic operations on the initial population to obtain the target population, and use the chromosomes in the target population whose fitness values ​​are greater than the first fitness threshold as the target solution set.

[0047] In this embodiment, genetic operations are performed on the initial population to obtain the target population, specifically including:

[0048] Calculate the fitness of all chromosomes in the initial population, and select the parent population based on the fitness of all chromosomes to obtain the parent population;

[0049] Under the constraint of the target energy consumption boundary, crossover operation is performed on the parent population to obtain the offspring population;

[0050] Determine the mutation probability of the coding segment, perform segmented mutation on the offspring population based on the mutation probability of the coding segment, verify the mutated chromosomes, and take the mutated chromosomes that pass the verification as the mutated population.

[0051] The target population is obtained based on elite individuals and variant populations in the parent population.

[0052] In this embodiment, the fitness of all chromosomes in the initial population is calculated, specifically including:

[0053] All chromosomes were analyzed and the overall energy utilization efficiency of the park, the total actual energy consumption cost of the park, and the actual access ratio of renewable energy were calculated.

[0054] Based on the park's comprehensive energy utilization efficiency, the park's actual total energy consumption cost, and the actual proportion of renewable energy access, the fitness of all chromosomes in the initial population is calculated using a fitness function.

[0055] The fitness function is:

[0056]

[0057] in, This represents the fitness value corresponding to chromosome x. , and All are weighting coefficients, where the weighting coefficients are... , and All are real numbers in the interval 0 to 1, and satisfy the following conditions: The specific value can be determined based on the park's energy management needs (such as prioritizing cost control). The value is higher than other weighting coefficients, emphasizing the use of renewable energy. The value is higher than other weighting coefficients, and is determined in combination with the scores from park operation and maintenance experts; The overall energy utilization efficiency of the park for chromosome X. To achieve the theoretically optimal comprehensive energy utilization efficiency of the park, The total energy consumption budget preset for the park, Let X be the total actual energy consumption cost of the campus for chromosome X. This represents the actual proportion of renewable energy connected to the grid corresponding to chromosome x. Let be the penalty weight for the t-th constraint. The total number of boundary constraints for the target energy use. Let be the violation coefficient of the energy boundary constraint on chromosome x for the t-th item, where the constraint is not violated. =0, for partial violations, linearly mapped to the 0~1.0 interval according to the actual degree of violation. The maximum constraint violation benchmark value, representing the upper limit threshold of the coefficient when the constraint is completely violated, is set to 1.0. In this embodiment, the parent population is a high-quality population selected from the initial population for genetic crossover, obtained based on the chromosome fitness. The offspring population is a new population generated from the parent population through crossover, inheriting some gene characteristics from the parent. The mutated population is the offspring population after segmented mutation and verification, possessing new gene combinations. Elite individuals are chromosomes with high fitness rankings (greater than the preset fitness threshold) in the parent population, used to retain high-quality genes. The coding segment mutation probability is a mutation probability set for different coding segments of chromosomes, adapting to the characteristics of each coding segment. The first fitness threshold is a critical value for selecting the target solution set; chromosomes with a value higher than this are selected. The fitness function is a mathematical model that quantifies the quality of chromosomes, calculating fitness by comprehensively considering multiple dimensions. The weighting coefficient is a coefficient that balances comprehensive energy utilization efficiency, cost, and other indicators to ensure balanced multi-objective optimization. The maximum constraint violation benchmark value is a benchmark value with unified dimensions for the constraint violation penalty term.

[0058] Genetic operations must balance population diversity with the preservation of high-quality genes to avoid optimization getting trapped in local optima. Fitness calculations can accurately assess chromosome quality, providing a basis for population selection. Selecting parental populations focuses on high-quality individuals, ensuring a solid genetic foundation; crossover operations introduce new gene combinations, enriching population diversity; segmented mutation sets probabilities for different coding segments to avoid excessive mutation of critical codes; verification steps ensure that mutated chromosomes meet constraints, avoiding invalid solutions. Preserving elite individuals prevents the loss of high-quality genes; the fitness function integrates multiple objectives and constraint penalties, ensuring that the optimization direction aligns with core energy management needs. Through multi-step collaborative improvements, the quality of the target population is enhanced, providing support for subsequent selection of high-quality target solutions.

[0059] For example, in this embodiment, each chromosome in the initial population can be analyzed first to extract parameters such as the equipment type operating time and energy allocation ratio of each business zone. Combined with basic information such as the energy consumption curve of the park equipment data, the comprehensive energy utilization efficiency of the park, the total actual energy consumption cost of the park, and the actual access ratio of renewable energy corresponding to each chromosome can be calculated.

[0060] In this embodiment, the three indicators mentioned above can be substituted into the fitness function, combined with preset weight coefficients and the maximum constraint violation benchmark value, to calculate the fitness of all chromosomes. Based on fitness, an elite retention strategy and roulette wheel selection are adopted to retain the top 10% of chromosomes in terms of fitness as elite individuals, and then the remaining individuals are selected according to their fitness percentage to form the parent population.

[0061] Under the constraint of the target energy consumption boundary, this embodiment can perform crossover operations on the parent population. For example, while keeping the business type partition identifier segment and verification segment unchanged, the equipment type coding segment is randomly segmented and recombined to generate the offspring population. The mutation probability of the differentiated coding segments is set as follows: the equipment type coding segment is set to 0.02, the runtime coding segment is set to 0.05, and the energy allocation ratio coding segment is set to 0.08. The offspring population is then subjected to segmented mutation.

[0062] After mutation, this embodiment can verify whether the chromosome meets the target energy boundary constraints through a check segment, remove non-compliant individuals, and form a mutated population. Finally, the elite individuals in the parent population are merged with the mutated population, duplicate chromosomes are removed, and a target population with the same size as the initial population is formed. Chromosomes with fitness values ​​greater than the first fitness threshold are selected as the target solution set.

[0063] This embodiment constructs a fitness function using multi-dimensional indicators to ensure comprehensive and accurate evaluation of chromosome quality, avoiding optimization biases caused by single indicators. In genetic operations, parent selection focuses on high-quality genes, crossover mutation enriches population diversity, segmented mutation and verification ensure the validity of the solution, and elite individuals are preserved to prevent the loss of superior features. The resulting target population is of high quality, with a target solution set exhibiting strong adaptability and compliance, providing a high-quality foundation for subsequent particle swarm optimization and improving the scientific rigor and efficiency of energy management optimization.

[0064] S104: Construct an initial particle swarm based on the target solution set, and iteratively update the initial particle swarm until the iterative convergence condition is met; the particles in the constructed initial particle swarm correspond one-to-one with the chromosomes in the target solution set, and the parameters included in each constructed particle are the equipment operating power of each business zone, the charging and discharging power of energy storage equipment, the renewable energy access regulation power, and the peak and valley time energy consumption allocation coefficient.

[0065] In this embodiment, the initial particle swarm is the first-generation particle set of the particle swarm optimization algorithm constructed based on the target solution set, used for subsequent continuous parameter precision optimization. Particles are the basic optimization units of the particle swarm optimization algorithm, carrying specific energy consumption control parameters. Equipment operating power is the power value of energy-consuming equipment in each business area during operation, directly related to energy consumption levels. The charging and discharging power of energy storage equipment is the power parameter during the charging and discharging process of the energy storage system, used to balance energy supply and demand. Renewable energy access regulation power is the power value of renewable energy such as photovoltaic and wind power connected to the park in real time, adapting to energy output fluctuations. Peak-valley energy allocation coefficient is a parameter that divides the energy consumption ratio between peak and valley periods, adapting to peak-valley electricity price differences. Iterative update is a cyclical process of dynamically adjusting particle parameters through the algorithm to approach the optimal solution. The iterative convergence condition is the criterion for determining whether the iteration stops, ensuring that the optimization achieves a stable effect.

[0066] The chromosomes of the target solution set have determined macroscopically feasible solutions (equipment type, runtime, etc.), but they lack precise control over continuous parameters. Particle swarm optimization (PSO) excels at continuous space optimization. Each particle corresponds one-to-one with a chromosome, allowing for refined control within the macroscopically feasible framework, preventing parameter optimization from deviating from constraints. The parameters contained in the particles are core continuous variables for energy management, directly affecting energy efficiency, cost, and energy balance. Iterative updates gradually optimize parameter values, and iterative convergence conditions prevent ineffective iterations, balancing optimization effectiveness and efficiency. Precise control of continuous parameters further enhances the scientific rigor and practicality of energy management solutions.

[0067] For example, in this embodiment, each chromosome in the target solution set can be analyzed first to extract the corresponding macroscopic parameters such as the equipment type, operating period, and energy allocation ratio of the business sector, and these parameters can be used as the parameter constraint boundary of the corresponding particle to ensure that the particle parameter optimization does not deviate from the feasible framework.

[0068] This embodiment can construct corresponding particles for each chromosome. Specifically, the operating power of the equipment is based on the rated power of the corresponding equipment, combined with the energy consumption characteristics of the business area, and the initial value is set within the range of 60%-100% of the rated power; the charging and discharging power of the energy storage equipment is referenced with reference to the rated power of the energy storage equipment and the historical load fluctuation data of the park, and the initial value of the charging power is set to 30%-70% of the rated power, and the initial value of the discharging power is set to 20%-60% of the rated power; the renewable energy access regulation power is based on the energy allocation ratio and the installed capacity of renewable energy, and the initial value is set according to the power range corresponding to the allocation ratio; the peak and valley energy consumption allocation coefficient is combined with the historical peak and valley energy consumption ratio, and the initial coefficient for the peak period is set to 0.3-0.5, and the initial coefficient for the valley period is set to 0.5-0.7.

[0069] All constructed particles are integrated to form an initial particle swarm. This embodiment can initiate an iterative update process. In each iteration, parameters are dynamically adjusted based on the particle fitness calculation results. Specifically, the operating power of equipment is optimized based on the real-time load of the park, the charging and discharging power of energy storage is adjusted according to the energy supply and demand gap, the access power is adjusted according to the real-time output of renewable energy, and the energy allocation coefficient is optimized in combination with the peak-valley electricity price difference. After each update, it is determined whether the iterative convergence condition is met. If the fluctuation of the globally optimal particle parameters is less than a preset threshold for 5 consecutive iterations, the iteration stops; if not, the iteration continues until the iterative convergence condition is met.

[0070] S105: The particle with the highest fitness during the iterative update process is used as the target particle, and energy consumption management of the smart park is carried out based on the parameters of the target particle.

[0071] In this embodiment, the target particle is the particle with the highest fitness during the initial particle swarm iteration update process. It carries the optimal continuous parameters for smart park energy management and is the core output of the algorithm optimization, directly guiding the actual energy management work in the park and ensuring the optimality of the management strategy. The iterative update has achieved precise parameter optimization through the particle swarm algorithm, with the particle with the highest fitness corresponding to the optimal energy management solution. Using it as the target particle, the algorithm optimization results can be transformed into implementable management strategies, allowing each business area to use energy according to optimal parameters, ensuring that energy management achieves the core goals of high efficiency, low cost, and compliance.

[0072] For example, this embodiment can, after the initial particle swarm iteration update meets the convergence condition, summarize the fitness records of all particles during the iteration process and select the particle with the highest fitness value as the target particle. This embodiment can analyze the various parameters of the target particle to clarify the specific values ​​of equipment operating power, energy storage equipment charging and discharging power, renewable energy access regulation power, and peak-valley energy consumption allocation coefficient for each business zone. This embodiment can synchronize these parameters to the park's energy management platform and execute them according to business zones. For example, the industrial zone schedules production energy-consuming equipment according to the optimal equipment operating power, the office zone adjusts the operating power of air conditioning and lighting according to parameters, and the commercial zone optimizes the power consumption structure by adapting the peak-valley energy consumption allocation coefficient. The effect of parameter execution is monitored in real time throughout the process to ensure that energy consumption management is implemented according to the optimal strategy.

[0073] As can be seen from the above, this embodiment first divides the area into zones based on the differences in business types (industrial, office, and commercial), then formulates exclusive energy consumption constraints for each zone, and binds the zone identifier with key information such as equipment type and operating time through chromosome coding. This achieves precise matching of the energy consumption characteristics of different zones, solving the problem of insufficient adaptability of traditional solutions. This embodiment first determines feasible basic solutions through reasonable screening, and then precisely optimizes detailed parameters such as equipment operating power and energy storage scheduling. This two-step collaboration can effectively cope with complex scenarios involving multiple coupled links, ensuring that the parameters of each energy consumption link are in an optimal state, reducing energy waste from the source.

[0074] In summary, this embodiment, through its zoned adaptation and precise control design, can ensure that the energy consumption of the park meets various constraints while minimizing overall energy costs, achieving the dual goals of efficient energy utilization and reasonable cost control. It provides a simple, feasible, and effective energy management solution for smart parks.

[0075] In one embodiment of this application, the analysis of all chromosomes and the calculation of the park's overall energy utilization efficiency and the park's actual total energy consumption cost include:

[0076] The business type partition identifier segment and equipment type code segment of all chromosomes are parsed to obtain the energy consumption equipment of all business type partitions in the park and each business type partition.

[0077] Retrieve the equipment operating parameters corresponding to each energy-consuming device from the equipment parameter database. The equipment operating parameters include load rate and energy efficiency curve, and energy conversion efficiency.

[0078] The runtime segment coding segment and energy allocation ratio coding segment of all chromosomes are parsed to obtain the device runtime segment and the proportion of each energy type for each energy-consuming device.

[0079] For each energy-consuming device, calculate the theoretical total input energy based on the device's operating time; determine the energy loss coefficient based on the proportion of each energy type corresponding to the device; calculate the actual total input energy based on the energy loss coefficient and the theoretical total input energy; and calculate the effective energy utilization of the device based on the actual total input energy and the device's operating parameters.

[0080] The overall energy efficiency of the park is calculated based on the effective energy utilization and actual total energy input of all equipment corresponding to all business zones.

[0081] For each business segment, obtain the electricity consumption and electricity price of all energy-consuming devices in that business segment during the device's operating period; calculate the grid electricity cost based on the electricity consumption and electricity price; calculate the renewable energy cost and energy storage cost according to the operating period of each energy-consuming device in that business segment and the proportion of each energy type.

[0082] The total actual energy cost of the park is calculated based on the grid electricity cost, renewable energy cost, and energy storage cost corresponding to all business zones.

[0083] In this embodiment, all chromosomes are analyzed and the actual renewable energy integration ratio is calculated, specifically including:

[0084] The energy allocation ratio coding segments of all chromosomes are analyzed to obtain the renewable energy allocation ratio ranges for each business sector.

[0085] The main renewable energy periods are selected from the operating periods of various energy-consuming equipment; the actual renewable energy access volume for each main renewable energy period is calculated based on the renewable energy installed capacity of all business sectors and the renewable energy allocation ratio range of all business sectors.

[0086] The actual renewable energy integration ratio is calculated based on the actual total energy input and the actual renewable energy integration volume.

[0087] In this embodiment, equipment operating parameters are the core technical parameters of energy-consuming equipment, including load factor and energy efficiency curves, and energy conversion efficiency, used to calculate energy utilization-related indicators. The load factor and energy efficiency curve reflects the relationship between equipment load and energy efficiency; energy conversion efficiency is the proportion of input energy converted into effective energy by the equipment. The theoretical total input energy is the energy input under ideal conditions calculated based on the equipment's operating period; the energy loss coefficient is the loss ratio during energy transmission and conversion; and the actual total input energy is the actual energy input after deducting losses from the theoretical value. Effectively utilized energy is the amount of energy actually used by the equipment. Electricity consumption is the electricity used during equipment operation; electricity price is the standard price for electricity; grid electricity cost is the cost of using grid electricity. Renewable energy cost is the related cost of utilizing renewable energy; energy storage cost is the cost of using an energy storage system. The renewable energy allocation ratio range is the range of renewable energy proportions in each zone; the renewable energy main period is the period when renewable energy output is stable and its proportion is high; renewable energy installed capacity is the total installed power of renewable energy equipment in the park; and the actual renewable energy access amount is the amount of renewable energy actually accessed by the park during the period.

[0088] The park's comprehensive energy utilization efficiency, actual total energy cost, and actual renewable energy integration ratio are core indicators for evaluating the merits of energy consumption schemes. Their calculations must be based on key parameters obtained through chromosome analysis and the actual characteristics of the equipment to ensure data accuracy and reliability. Obtaining basic decision parameters through chromosome analysis and retrieving core equipment parameters from the equipment parameter database ensures the relevance of the calculations. Introducing an energy loss coefficient reflects the actual losses in energy transmission and conversion, making the total input energy calculation more realistic. Screening for peak renewable energy periods matches the output characteristics of renewable energy, ensuring accurate integration ratio calculations. Multi-dimensional cost breakdown and comprehensive summarization comprehensively reflect the composition of energy consumption costs, providing comprehensive and accurate input data for the fitness function and supporting subsequent optimization decisions.

[0089] For example, in this embodiment, each chromosome in the initial population can be analyzed first. From the business type identification segment and equipment type coding segment, the industrial, office and commercial business types in the park and their corresponding energy-consuming equipment can be determined, such as air compressors in the industrial zone and central air conditioning in the office zone. From the operating time coding segment, the operating time of the air compressor is obtained as 8:00-22:00 and the operating time of the central air conditioning is obtained as 9:00-18:00. From the energy allocation ratio coding segment, the proportion of grid electricity in the industrial zone is obtained as 60%, the proportion of renewable energy is 25%, and the proportion of energy storage is 15%.

[0090] This embodiment can retrieve the load rate and energy efficiency curves of the air compressor, the energy conversion efficiency of 85%, and the corresponding parameters of the central air conditioning from the equipment parameter database. For the air compressor, this embodiment can calculate the theoretical total input energy of 1400kWh based on a rated power of 100kW and 14 hours of operation. This embodiment can determine the energy loss coefficient of 5% based on the transmission loss of renewable energy and energy storage, and calculate the actual total input energy of 1400×(1+5%)=1470kWh. Combining the energy efficiency of 82% corresponding to a load rate of 70%, this embodiment can calculate the effective energy utilization of 1470×82%=1205.4kWh. Similarly, the relevant data of all equipment are calculated, and the effective energy utilization of all equipment in all business zones is summarized with the actual total input energy to obtain the overall energy utilization efficiency of the park.

[0091] For industrial zones, this embodiment can calculate the electricity consumption of equipment such as air compressors during operation, which is 1000 kWh, and calculate the grid electricity cost based on peak and off-peak electricity prices; calculate the renewable energy cost based on a renewable energy ratio of 25% and an operation and maintenance fee rate of 0.05 yuan / kWh; calculate the energy storage cost based on an energy storage ratio of 15% and a depreciation + loss fee rate of 0.1 yuan / kWh; and summarize the three types of costs for all zones to obtain the total actual energy consumption cost of the park.

[0092] This embodiment can analyze the renewable energy allocation ratio range of each zone, select the stable photovoltaic output period of 9:00-16:00 as the main period, and calculate the actual renewable energy access amount of 100×0.8×7=560kWh based on the park's photovoltaic installed capacity of 100kW and output coefficient of 0.8. Combining the actual total energy input of all equipment, the actual renewable energy access ratio can be calculated.

[0093] In this embodiment, the overall energy utilization efficiency of the park is calculated based on the effective energy utilization and actual total energy input of all equipment corresponding to all business zones, specifically including:

[0094] Based on the effective energy utilization and actual total energy input of all equipment corresponding to all business zones, the overall energy utilization efficiency of the park is calculated using the first formula.

[0095] The first formula is:

[0096]

[0097] in, To improve the overall energy efficiency of the park, Let e ​​be the effective energy utilization of the e-th energy-consuming device in the p-th business sector corresponding to chromosome x. Let P be the total input energy of the e-th energy-consuming device in the p-th business type zone corresponding to chromosome x, where P is the total number of business type zones covered by chromosome x. Let be the number of core energy-consuming devices in the p-th business sector.

[0098] In this embodiment, the actual total energy consumption cost of the park is calculated based on the grid electricity cost, renewable energy cost, and energy storage cost corresponding to all business types and zones. Specifically, this includes:

[0099] Based on the grid electricity cost, renewable energy cost, and energy storage cost corresponding to all business zones, the actual total energy consumption cost of the park is calculated using the second formula.

[0100] The second formula is:

[0101]

[0102] in, This represents the total actual energy consumption cost of the park. Let $ be the electricity usage cost of the power grid in the p-th business sector corresponding to chromosome x. Let $\frac{x}{x}$ be the renewable energy operation and maintenance cost for the $p$-th business sector corresponding to chromosome $x$. Let $\frac{x}{x}$ be the energy storage system usage cost for the p-th business sector corresponding to chromosome $x$. Let $\frac{ ...

[0103] In this embodiment, the actual renewable energy integration ratio is calculated based on the actual total input energy and the actual renewable energy integration amount, specifically including:

[0104] Based on the actual total input energy and the actual amount of renewable energy connected, the actual proportion of renewable energy connected is calculated using the third formula.

[0105] The third formula is:

[0106]

[0107] in, The actual proportion of renewable energy connected to the grid. Let x be the amount of renewable energy actually connected to the p-th business sector. Let be the total energy consumption of the p-th business sector corresponding to chromosome x.

[0108] This embodiment achieves accurate calculation of core indicators by hierarchically analyzing key coding segments of chromosomes and combining equipment parameter databases with actual operating characteristics. Consideration of energy loss coefficients and peak renewable energy periods ensures the calculation results closely align with real-world scenarios, and cost breakdown calculations comprehensively cover various energy expenditures. The rigorous calculation logic and reliable data sources for the three major indicators provide high-quality input to the fitness function, ensuring the genetic algorithm accurately selects superior chromosomes. This lays a solid foundation for subsequent optimization and helps achieve the management goals of high energy efficiency, controllable costs, and a high proportion of green electricity.

[0109] Corresponding to the smart park energy management method in the above embodiment, Figure 2 This is a structural block diagram of a smart park energy management device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The smart park energy management device 20 includes: a business type zoning module 21, an initialization module 22, a genetic module 23, an optimization parameter module 24, and an energy management module 25.

[0110] Among them, the business format zoning module 21 is used to divide the smart park into multiple business format zones according to the regional business format type; the regional business format types include industrial type, office type and commercial type;

[0111] Initialization module 22 is used to determine the target energy consumption boundary constraints for multiple business type zones; based on the target energy consumption boundary constraints, the energy-consuming equipment type, operating period and energy allocation ratio of each business type zone are encoded using chromosomes to generate an initial population; the chromosome includes a business type zone identifier segment, equipment type encoding segment, operating period encoding segment, energy allocation ratio encoding segment and verification segment;

[0112] Genetic module 23 is used to perform genetic operations on the initial population to obtain the target population, and to take the chromosomes in the target population whose fitness values ​​are greater than the first fitness threshold as the target solution set;

[0113] The optimization parameter module 24 is used to construct an initial particle swarm based on the target solution set and iteratively update the initial particle swarm until the iterative convergence condition is met. The particles in the constructed initial particle swarm correspond one-to-one with the chromosomes in the target solution set. The parameters included in each constructed particle are the equipment operating power of each business zone, the charging and discharging power of the energy storage equipment, the renewable energy access regulation power, and the peak and valley time energy consumption allocation coefficient.

[0114] The energy management module 25 is used to manage the energy consumption of the smart park based on the parameters of the target particle, with the particle having the highest fitness during the iterative update process as the target particle.

[0115] In one embodiment of this application, when determining the target energy consumption boundary constraints of multiple business type zones, the initialization module 22 is specifically used to: acquire equipment data, energy consumption curves, energy consumption boundary data and energy resource data of multiple business type zones;

[0116] For each business type zone, the energy consumption curves corresponding to that business type zone are divided into a first energy consumption curve set and a second energy consumption curve set according to the scenario identifier. The scenario identifier of the first energy consumption curve set is the weekday scenario, and the scenario identifier of the second energy consumption curve set is the holiday scenario. The first energy consumption characteristic is obtained based on the first energy consumption curve set, and the second energy consumption characteristic is obtained based on the second energy consumption curve set. The target energy consumption boundary constraint of that business type zone is determined based on the first energy consumption characteristic, the second energy consumption characteristic, equipment data, energy consumption boundary data, and energy resource data.

[0117] In one embodiment of this application, when the initialization module 22 determines the target energy boundary constraint of the business type zone based on the first energy consumption feature, the second energy consumption feature, equipment data, energy boundary data, and energy resource data, it is specifically used to: determine the first energy boundary constraint based on the first energy consumption feature, equipment data, energy boundary data, and energy resource data; determine the second energy boundary constraint based on the second energy consumption feature, equipment data, energy boundary data, and energy resource data; and use the first energy boundary constraint and the second energy boundary constraint as the target energy boundary constraint.

[0118] In one embodiment of this application, when the genetic module 23 performs genetic operations on the initial population to obtain the target population, it is specifically used for:

[0119] Calculate the fitness of all chromosomes in the initial population, and select the parent population based on the fitness of all chromosomes to obtain the parent population;

[0120] Under the constraint of the target energy consumption boundary, crossover operation is performed on the parent population to obtain the offspring population;

[0121] Determine the mutation probability of the coding segment, perform segmented mutation on the offspring population based on the mutation probability of the coding segment, verify the mutated chromosomes, and take the mutated chromosomes that pass the verification as the mutated population.

[0122] The target population is obtained based on elite individuals and variant populations in the parent population.

[0123] In one embodiment of this application, the genetic module 23, when calculating the fitness of all chromosomes in the initial population, is specifically used for:

[0124] All chromosomes were analyzed and the overall energy utilization efficiency of the park, the total actual energy consumption cost of the park, and the actual access ratio of renewable energy were calculated.

[0125] Based on the park's comprehensive energy utilization efficiency, the park's actual total energy consumption cost, and the actual proportion of renewable energy access, the fitness of all chromosomes in the initial population is calculated using a fitness function.

[0126] The fitness function is:

[0127]

[0128] in, This represents the fitness value corresponding to chromosome x. , and All are weighting coefficients. , The overall energy utilization efficiency of the park for chromosome X. To achieve the theoretically optimal comprehensive energy utilization efficiency of the park, The total energy consumption budget preset for the park, Let X be the total actual energy consumption cost of the campus for chromosome X. This represents the actual proportion of renewable energy connected to the grid corresponding to chromosome x. Let be the violation coefficient of the energy boundary constraint applied to chromosome x for item t. Let be the penalty weight for the t-th constraint. The total number of boundary constraints for the target energy use. The maximum constraint violation of the benchmark value.

[0129] In one embodiment of this application, the genetic module 23, when analyzing all chromosomes and calculating the overall energy utilization efficiency of the park and the total actual energy consumption cost of the park, is specifically used for:

[0130] The business type partition identifier segment and equipment type code segment of all chromosomes are parsed to obtain the energy consumption equipment of all business type partitions in the park and each business type partition.

[0131] Retrieve the equipment operating parameters corresponding to each energy-consuming device from the equipment parameter database. The equipment operating parameters include load rate and energy efficiency curve, and energy conversion efficiency.

[0132] The runtime segment coding segment and energy allocation ratio coding segment of all chromosomes are parsed to obtain the device runtime segment and the proportion of each energy type for each energy-consuming device.

[0133] For each energy-consuming device, calculate the theoretical total input energy based on the device's operating time; determine the energy loss coefficient based on the proportion of each energy type corresponding to the device; calculate the actual total input energy based on the energy loss coefficient and the theoretical total input energy; and calculate the effective energy utilization of the device based on the actual total input energy and the device's operating parameters.

[0134] The overall energy efficiency of the park is calculated based on the effective energy utilization and actual total energy input of all equipment corresponding to all business zones.

[0135] For each business segment, obtain the electricity consumption and electricity price of all energy-consuming devices in that business segment during the device's operating period; calculate the grid electricity cost based on the electricity consumption and electricity price; calculate the renewable energy cost and energy storage cost according to the operating period of each energy-consuming device in that business segment and the proportion of each energy type.

[0136] The total actual energy cost of the park is calculated based on the grid electricity cost, renewable energy cost, and energy storage cost corresponding to all business zones.

[0137] In one embodiment of this application, the genetic module 23, when analyzing all chromosomes and calculating the actual renewable energy access ratio, is specifically used for:

[0138] The energy allocation ratio coding segments of all chromosomes are analyzed to obtain the renewable energy allocation ratio ranges for each business sector.

[0139] The main renewable energy periods are selected from the operating periods of various energy-consuming equipment; the actual renewable energy access volume for each main renewable energy period is calculated based on the renewable energy installed capacity of all business sectors and the renewable energy allocation ratio range of all business sectors.

[0140] The actual renewable energy integration ratio is calculated based on the actual total energy input and the actual renewable energy integration volume.

[0141] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the business format partitioning module 21, initialization module 22, genetic module 23, optimization parameter module 24, and energy consumption management module 25 are shown.

[0142] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0143] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0144] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information about smart campus devices.

[0145] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the embodiments of the smart park energy consumption management method provided in this application, or they can execute the implementation methods of the electronic device 300 described in the embodiments of this application, which will not be repeated here.

[0146] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0147] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0148] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0151] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments in this application, depending on actual needs.

[0152] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or as software functional modules / units.

[0153] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart park energy consumption management method, characterized in that, include: The smart park is divided into multiple business type zones based on the regional business type; the regional business type includes industrial, office and commercial types. Determine the target energy consumption boundary constraints for multiple business sector zones; based on the target energy consumption boundary constraints, perform chromosome encoding on the energy-consuming equipment type, operating period, and energy allocation ratio of each business sector zone to generate an initial population; the chromosome includes a business sector zone identifier segment, equipment type encoding segment, operating period encoding segment, energy allocation ratio encoding segment, and verification segment; The fitness of all chromosomes in the initial population is calculated. Based on the fitness of all chromosomes, a parent population is selected to obtain the parent population. Under the target energy boundary constraint, a crossover operation is performed on the parent population to obtain the offspring population. The mutation probability of the coding segment is determined. Based on the mutation probability of the coding segment, segment mutation is performed on the offspring population. The mutated chromosomes are verified, and the mutated chromosomes that pass the verification are used as the mutated population. A target population is obtained based on the elite individuals in the parent population and the mutated population. Chromosomes in the target population with fitness values ​​greater than a first fitness threshold are used as the target solution set. An initial particle swarm is constructed based on the target solution set, and the initial particle swarm is iteratively updated until the iterative convergence condition is met. The particles in the constructed initial particle swarm correspond one-to-one with the chromosomes in the target solution set. The parameters included in each constructed particle are the equipment operating power of each business zone, the charging and discharging power of energy storage equipment, the renewable energy access regulation power, and the peak and valley time energy consumption allocation coefficient. The particle with the highest fitness during the iterative update process is used as the target particle, and energy consumption management is performed on the smart park based on the parameters of the target particle. The calculation of the fitness of all chromosomes in the initial population includes: analyzing all chromosomes and calculating the park's comprehensive energy utilization efficiency, the park's actual total energy consumption cost, and the actual proportion of renewable energy access; based on the park's comprehensive energy utilization efficiency, the park's actual total energy consumption cost, and the actual proportion of renewable energy access, calculating the fitness of all chromosomes in the initial population using a fitness function; the fitness function is: in, This represents the fitness value corresponding to chromosome x. , and All are weighting coefficients. , The overall energy utilization efficiency of the park for chromosome X. To achieve the theoretically optimal comprehensive energy utilization efficiency of the park, The total energy consumption budget preset for the park, Let X be the total actual energy consumption cost of the campus for chromosome X. This represents the actual proportion of renewable energy connected to the grid corresponding to chromosome x. Let be the violation coefficient of the energy boundary constraint applied to chromosome x for item t. Let be the penalty weight for the t-th constraint. The total number of boundary constraints for the target energy use. The maximum constraint violation of the benchmark value.

2. The smart park energy consumption management method as described in claim 1, characterized in that, The determination of target energy consumption boundary constraints for multiple business type zones includes: Acquire equipment data, energy consumption curves, energy consumption boundary data, and energy resource data from multiple business sectors; For each business type zone, the energy consumption curves corresponding to that business type zone are divided into a first energy consumption curve set and a second energy consumption curve set according to the scene identifier; the scene identifier of the first energy consumption curve set is a weekday scene, and the scene identifier of the second energy consumption curve set is a holiday scene; a first energy consumption feature is obtained based on the first energy consumption curve set, and a second energy consumption feature is obtained based on the second energy consumption curve set; the target energy consumption boundary constraint of that business type zone is determined based on the first energy consumption feature, the second energy consumption feature, the equipment data, the energy consumption boundary data, and the energy resource data.

3. The smart park energy consumption management method as described in claim 2, characterized in that, The determination of the target energy consumption boundary constraint for the business type zone based on the first energy consumption characteristic, the second energy consumption characteristic, the equipment data, the energy consumption boundary data, and the energy resource data includes: The first energy consumption boundary constraint is determined based on the first energy consumption characteristic, the equipment data, the energy consumption boundary data, and the energy resource data; The second energy consumption boundary constraint is determined based on the second energy consumption characteristic, the equipment data, the energy consumption boundary data, and the energy resource data; The first energy consumption boundary constraint and the second energy consumption boundary constraint are used as the target energy consumption boundary constraints.

4. The smart park energy consumption management method as described in claim 1, characterized in that, All chromosomes were analyzed, and the overall energy utilization efficiency of the park and the total actual energy consumption cost of the park were calculated, including: The business type partition identifier segment and equipment type code segment of all chromosomes are parsed to obtain the energy consumption equipment of all business type partitions in the park and each business type partition. Retrieve the equipment operating parameters corresponding to each energy-consuming device from the equipment parameter database. The equipment operating parameters include load rate and energy efficiency curve, and energy conversion efficiency. The runtime segment coding segment and energy allocation ratio coding segment of all chromosomes are parsed to obtain the device runtime segment and the proportion of each energy type for each energy-consuming device. For each energy-consuming device, the theoretical total input energy is calculated based on the device's operating period; the energy loss coefficient is determined based on the proportion of each energy type corresponding to the energy-consuming device; the actual total input energy is calculated based on the energy loss coefficient and the theoretical total input energy; and the effective energy utilization is calculated based on the actual total input energy and the device's operating parameters. The overall energy efficiency of the park is calculated based on the effective energy utilization and actual total energy input of all equipment corresponding to all business zones. For each business segment, obtain the electricity consumption and electricity price of all energy-consuming devices in that business segment during the device's operating period; calculate the grid electricity cost based on the electricity consumption and the electricity price; calculate the renewable energy cost and energy storage cost according to the operating period of each energy-consuming device in that business segment and the proportion of each energy type. The total actual energy consumption cost of the park is calculated based on the grid electricity cost, renewable energy cost, and energy storage cost corresponding to all business zones.

5. The smart park energy consumption management method as described in claim 4, characterized in that, All chromosomes were analyzed and the actual renewable energy integration ratio was calculated, including: The energy allocation ratio coding segments of all chromosomes are analyzed to obtain the renewable energy allocation ratio ranges for each business sector. The main renewable energy periods are selected from the operating periods of various energy-consuming equipment; the actual renewable energy access volume for each main renewable energy period is calculated based on the renewable energy installed capacity of all business sectors and the renewable energy allocation ratio range of all business sectors. The actual renewable energy access ratio is calculated based on the actual total input energy and the actual renewable energy access volume.

6. A smart park energy management device, characterized in that, include: The business type zoning module is used to divide the smart park into multiple business type zones based on the regional business type; the regional business type includes industrial type, office type and commercial type; An initialization module is used to determine the target energy consumption boundary constraints for multiple business type zones; based on the target energy consumption boundary constraints, the energy-consuming equipment type, operating period, and energy allocation ratio of each business type zone are encoded using chromosomes to generate an initial population; the chromosomes include a business type zone identifier segment, an equipment type encoding segment, an operating period encoding segment, an energy allocation ratio encoding segment, and a verification segment; The genetic module is used to calculate the fitness of all chromosomes in the initial population, select parent populations based on the fitness of all chromosomes to obtain the parent population; perform crossover operation on the parent population under the target energy boundary constraint to obtain the offspring population; determine the mutation probability of the coding segment, perform segmented mutation on the offspring population based on the mutation probability of the coding segment, verify the mutated chromosomes, and take the mutated chromosomes that pass the verification as the mutant population; obtain the target population based on the elite individuals in the parent population and the mutant population; and take the chromosomes in the target population whose fitness value is greater than a first fitness threshold as the target solution set. The genetic module is specifically used to: analyze all chromosomes and calculate the park's overall energy utilization efficiency, the park's actual total energy consumption cost, and the actual proportion of renewable energy access; Based on the comprehensive energy utilization efficiency of the park, the actual total energy consumption cost of the park, and the actual integration ratio of renewable energy, the fitness of all chromosomes in the initial population is calculated using a fitness function; the fitness function is: in, This represents the fitness value corresponding to chromosome x. , and All are weighting coefficients. , The overall energy utilization efficiency of the park for chromosome X. To achieve the theoretically optimal comprehensive energy utilization efficiency of the park, The total energy consumption budget preset for the park, Let X be the total actual energy consumption cost of the campus for chromosome X. This represents the actual proportion of renewable energy connected to the grid corresponding to chromosome x. Let be the violation coefficient of the energy boundary constraint applied to chromosome x for item t. Let be the penalty weight for the t-th constraint. The total number of boundary constraints for the target energy use. The maximum constraint violation of the benchmark value; The optimization parameter module is used to construct an initial particle swarm based on the target solution set, and iteratively update the initial particle swarm until the iterative convergence condition is met. The particles in the constructed initial particle swarm correspond one-to-one with the chromosomes in the target solution set. The parameters included in each constructed particle are the equipment operating power of each business zone, the charging and discharging power of the energy storage equipment, the renewable energy access regulation power, and the peak and valley time energy consumption allocation coefficient. The energy management module is used to manage the energy consumption of the smart park based on the parameters of the target particle, with the particle having the highest fitness during the iterative update process as the target particle.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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