46 results about "Operation model" patented technology

The invention belongs to the field of intelligent scheduling of an energy system, and provides a coupling market operation optimization method and system based on a multi-agent game, a coupling market operation model is established through a power distribution market operation model and an operator operation model, and operation data among a power distribution market, an operator and a user market can be correlated, so that the operation efficiency of the power distribution market is improved. Furthermore, by obtaining a double-layer game model and a target function set, the electricity purchase cost of the user and operation target data of the active power distribution network and operator operation are correlated, and the target function set can be constrained and solved based on a target constraint function set; and the parameters of the double-layer game model and the coupling market operation model can be optimized according to the solving result of the target function, so that the stable operation of the whole coupling market can be ensured, and the operation requirements among a power distribution market, an operator and a user market can be considered.

The invention discloses an industrial park comprehensive energy system low-carbon planning method considering a production process, and the method comprises the following steps: 1, obtaining the production line condition of an industrial park, and constructing a production line model; 2, determining to-be-planned equipment and parameters, and constructing a lower-layer planning operation model considering short-term uncertainty; 3, determining medium and long term uncertainty parameters, and constructing a medium and long term uncertainty set; step 4, determining a multi-objective optimization model; 5, searching a Pareto leading edge by using multi-target reinforcement learning based on a decomposition technology; and 6, selecting a proper Pareto frontier solution, and substituting the Pareto frontier solution into the lower-layer planning operation model to obtain a corresponding planning result. The invention aims to realize low-carbon transformation of an integrated energy system (IRIES) in an industrial area so as to cope with carbon reduction, emission reduction and challenges in a carbon dense area.

The invention provides an operation optimization method and system for an energy storage participation auxiliary service market, and the method comprises the steps: determining an energy storage performance aging factor, and defining an energy storage aging time function according to the energy storage performance aging factor; the energy storage performance aging factors comprise calendar aging and cycle aging; based on the energy storage aging time function, constructing an intra-day operation model taking energy storage expected life maximization and intra-day benefit maximization as objective functions; and solving the intra-day operation model by adopting a multi-agent algorithm of a non-adversarial game to obtain an optimal strategy of energy storage intra-day operation. According to the invention, the relation between the energy storage aging characteristic and the working characteristic is introduced to carry out the energy storage intra-day operation decision, the economic benefit can be truly evaluated, the energy efficiency of energy storage and the stability of scheduling are considered, and the operation maintenance level is greatly improved.

The invention relates to a power distribution network variable time scale optimization method considering source-load-storage collaborative optimization. The method specifically comprises the following steps: step 1, constructing an excitation type load demand corresponding model, an energy storage comprehensive operation model, a day-ahead scheduling stage model and an intra-day multi-time scale scheduling model; step 2, through day-ahead-day-real-time continuous fine scheduling, fully excavating the adjustment potential of the energy storage device and the flexible load; 3, introducing a deviation reduction rate-cost increase rate dynamic game model through scheduling of a variable time scale, and adaptively adjusting the time scale in a real-time stage; 4, constructing a real-time variable time scale scheduling model; 5, improving a rolling optimization strategy, and classifying various resources to match corresponding time scales; step 6, performing instance analysis; the method has the advantages that the consumption rate is increased, the time scale is adaptively adjusted, the power quality is improved, the dynamic game model is introduced, and cooperative scheduling is realized.

The invention discloses a benefit distribution model construction and collaborative optimization method under multiple operation modes of an industrial software service platform, and the method comprises the following steps: 1, analyzing an optimal decision based on a Stackelberg master-slave game decision model: 11) constructing a pricing model, profit functions and constraint conditions of the industrial software service platform and the industrial software developer are obtained respectively; 12) performing optimal game decision analysis; 121) analyzing optimal decisions of an industrial software developer and an industrial software service platform under an independent operation condition to obtain an optimal price and a maximum benefit; 122) analyzing the optimal decision of the industrial software developer and the industrial software service platform under the cooperative operation condition to obtain the overall maximum benefit; 2, establishing a benefit distribution model based on a Shapley value method; step 3, benefit decision optimization: 31) fitting a relation function between the price and the number of functional modules by using historical data; and 32) adopting a reinforcement learning method to decide an optimal benefit distribution scheme.

Technological improvements are described for automatically generating crew and equipment schedules that are optimized for regulatory compliance and balanced according to goals or policies referred to as balance settings. Example systems and methods include building and maintaining a compliance library of regulations. A machine-learning operations model generates a schedule that complies with regulations and receives input data including a proposed schedule and the priority-weighted balance settings. Then, the operations model, using a set of recursive processes, generates a candidate schedule that is optimized based on the regulations and the balance settings. An auditor confirms the candidate schedule complies with regulations. The auditor evaluates real-time data to flag potential violations and generate compliance notices and compliance reports. The system includes a generative AI model trained using an industry-specific small language model (ISLM) for receiving textual and image data for use by the operations model and for generating responsive messages.

The invention discloses an intelligent production scheduling method and system based on a fixed sequence genetic algorithm. The method comprises the following steps: dividing independent production areas of a workshop; key operation is determined, and modeling is carried out on time of the key operation; generating a process set containing a fixed sequence key job based on a process path, performing crossover variation by using an improved genetic algorithm, and keeping the sequence of internal processes of a task unchanged to avoid an invalid solution; and calculating the shortest processing duration through population iteration, and generating a production scheduling Gantt chart. The system comprises a workshop function area module, a key operation type module, a key operation modeling module and a production scheduling result output module, so as to realize the output of an intelligent production scheduling result. The method improves the production scheduling efficiency and the applicability of the production scheduling result, and is suitable for complex production scenes such as electronic assembly and cabinet debugging.

The invention relates to a supply chain modeling method based on a platform operation mode and a carbon transaction policy, and belongs to the technical field of supply chain design. According to the method, carbon emission data of each link of a supply chain is collected in real time through a multi-source sensor, the law of conservation of energy is used as a constraint condition to be embedded into a Transform-XL neural network model, a multi-link nonlinear coupling model is constructed, and a nonlinear coupling relational expression of carbon emission and sales is output. And in combination with a carbon quota distribution mechanism, solving a QUBO matrix by using a quantum annealing algorithm to obtain an optimal scale combination strategy of each link scale of the supply chain, and realizing collaborative optimization of carbon emission intensity reduction and profit improvement. According to the method, the problems of poor adaptability, low calculation efficiency and the like of a traditional supply chain optimization model under a dynamic carbon transaction policy are solved, and an efficient solution is provided for supply chain decision making.

The invention relates to a virtual power plant optimization scheduling method and system. The method comprises the following steps: establishing an operation model of a multi-type adjustable resource main body; constructing a master-slave game model of the aggregator and the resource main body, wherein the master-slave game model comprises an upper-layer model taking the aggregator as a leader and a lower-layer model taking the resource main body as a follower; solving a master-slave game model of the aggregator and the resource main body to obtain an optimal compensation price strategy and an optimal response power strategy, and constructing an optimal power scheduling plan according to the optimal response power strategy; the optimal power scheduling plan is used for being issued to each adjustable resource main body for execution so as to realize optimal scheduling of the virtual power plant. According to the method, the participation enthusiasm of the resource main body can be stimulated, the user participation degree can be improved, the scheduling instruction execution rate can be improved, the model accuracy can be improved, the cooperative capability of heterogeneous resources can be improved, and the overall operation economy and stability of the virtual power plant can be improved.

The present invention relates to a method for constructing digital ship intelligent manufacturing. The construction method comprises the following steps: digital dock construction; digital weight and center of gravity control; hull response monitoring system construction; operation and management integrated digital management platform construction: building a digital overall framework, mainly including a basic layer, an equipment resource layer, an execution basic layer, a coordination business layer, an operation management layer and a decision management layer; digital inspection management platform construction. The advantages of the present invention are: with the help of digital transformation, the components are based on engineering decomposition, with planning management as the command, and an integrated solution for overall design, materials, production, quality and cost management is constructed. Through the construction of the industrial Internet and the integrated application of the information system, a digital shipyard based on digital twins is constructed, facilities, equipment, resources, etc. are interconnected, early warning and analysis are carried out from multiple angles, a big data operation system is explored, a digital operation model is constructed, and finally a new digital production model is formed.

The present application relates to the technical field of retail management, and particularly refers to a lean operation model for clothing retail stores from the perspective of data empowerment. The model comprises a four-dimensional health degree evaluation module, a data report generation module, a store manager interview consensus module and a closed-loop execution review module. The four-dimensional health degree evaluation module constructs a mathematical model based on four dimensions of sales efficiency, human efficiency quality, member value and product efficiency and their subordinate indicators, and outputs an overall health degree score OHS to realize quality diagnosis of store operation. The data report generation module generates five key business reports to convert raw data into visual insights. The store manager interview consensus module realizes the integration of data and experience through a structured eight-step process and generates a SMART rectification plan. The closed-loop execution review module realizes the PDCA cycle through an agile review mechanism. The present application solves problems such as low operation efficiency, decision-making relying on experience and insufficient rectification execution of clothing retail stores through the deep integration of data-driven and lean operation.

Technological improvements are described for automatically generating crew and equipment schedules that are optimized for regulatory compliance and balanced according to goals or policies referred to as balance settings. Example systems and methods include building and maintaining a compliance library of regulations. A machine-learning operations model generates a schedule that complies with regulations and receives input data including a proposed schedule and the priority-weighted balance settings. Then, the operations model, using a set of recursive processes, generates a candidate schedule that is optimized based on the regulations and the balance settings. An auditor confirms the candidate schedule complies with regulations. The auditor evaluates real-time data to flag potential violations and generate compliance notices and compliance reports. The system includes a generative AI model trained using an industry-specific small language model (ISLM) for receiving textual and image data for use by the operations model and for generating responsive messages.

The invention discloses an integrated production operation platform based on digital and intelligent twinning, relates to the technical field of production operation, and establishes a digital and intelligent twinning operation model of a physical execution layer and a virtual mapping layer by constructing state characteristic parameters of operation nodes and fusing multi-source time sequence data. A state consistency constraint function is introduced to represent the synchronization deviation between the two layers; on the basis, performing real-time iterative calculation on the target job execution model to obtain a state deviation value of each job node, and checking state consistency based on signal sequential logic and a robust online monitoring algorithm to generate an abnormal state judgment result; further executing instruction delay, path re-planning or state rollback according to the exception type to realize dynamic correction and closed-loop control of the operation process; according to the invention, the accuracy of abnormity identification and the timeliness of processing in complex production operation can be effectively improved, and the stability and the safety of overall operation of the system are enhanced.

The invention discloses a time-varying load dynamic compensation motion control method and system based on UVW kernel alignment, and belongs to the technical field of precision machine.The time-varying load dynamic compensation motion control method based on UVW kernel alignment comprises the following steps that S1, a motion model is established, firstly, the motion model of a UVB platform is established, the motion model comprises motion equations and kinematics equations of all axes, and the motion equations of all the axes are calculated; meanwhile, key parts of the motion model are subjected to structure optimization, a foundation is laid for deformation error analysis and compensation, and the foundation is used for achieving dynamic compensation; s2, load change is monitored, load change information borne by the UVW platform is obtained in real time based on the sensing monitoring technology, and the obtained load change information serves as the basis of dynamic compensation. By optimizing the mechanical structures of key components of the motion model, the structural performance of the UVW platform can be improved, the material use amount is reduced, the machining technology is simplified, and the cost is reduced. The manufacturing cost is reduced, so that the flexibility of an operation model is improved, and deformation errors caused by self-weight are reduced.

The application relates to the technical field of equipment operation and maintenance decision and optimization, and discloses a multi-modal knowledge driven production equipment system multi-objective operation mode optimization method, which comprises the following steps: constructing a production demand change prediction model based on periodical trend decomposition; constructing a multi-modal knowledge graph model; constructing a production equipment system operation quality evaluation index system and determining optimization targets; constructing a data driven equipment operation model based on an extended random flow network model; evaluating each optimization target of the production equipment system; constructing a multi-objective reward function based on a knowledge driven Pareto distance method; constructing an operation and maintenance management and mode optimization joint strategy optimization model; identifying and selecting key optimization targets and operation and maintenance technologies based on multi-modal production demand and production equipment operation data driven multi-modal knowledge graphs; and performing production equipment mode optimization and operation and maintenance management strategy joint optimization by using a deep deterministic policy gradient, so that operation and maintenance strategy dynamic optimization solving is realized.

The invention discloses a multi-agricultural machinery collaborative operation method and system, electronic equipment and a storage medium, and the method comprises the steps: building a field operation model of a target farmland based on the boundary information and geographic information of the target farmland; dividing the field operation model into a plurality of operation sub-regions according to the operation capability parameters of each agricultural machine participating in the collaborative operation, allocating at least one operation sub-region to each agricultural machine, and generating an initial operation path covering all the corresponding operation sub-regions for each agricultural machine; and establishing communication connection among a plurality of agricultural machines participating in collaborative operation, and dynamically scheduling operation sub-region distribution and an initial operation path corresponding to each agricultural machine based on real-time position information and operation state information, shared in real time, of each agricultural machine. According to the method, the problems of unreasonable scheduling, frequent conflict of operation paths and insufficient dynamic adjustment capability in collaborative operation of the multiple agricultural machines are solved, and the collaborative operation efficiency and overall robustness of the multiple agricultural machines are greatly improved.

The invention relates to a capacity configuration method and device of a hydrogen production system, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of energy system optimization, and can improve the operation efficiency of the hydrogen production system. The method comprises the following steps: establishing a system operation model for representing the operation state of the hydrogen production system; obtaining an optimization target of capacity configuration according to operation resources consumed in the operation process of the hydrogen production system; according to the system operation model and the optimization target, determining candidate capacity configuration of each unit; according to the target quantized values of the plurality of candidate capacity configurations relative to the optimization target of the capacity configuration, determining the quality degrees corresponding to the plurality of candidate capacity configurations, and according to the quality degrees, determining the adjustment directions corresponding to the plurality of candidate capacity configurations; and performing configuration iteration adjustment of different adjustment degrees on the plurality of candidate capacity configurations according to each adjustment direction until an iteration ending condition is met, and determining respective target capacity configurations of the units.

The application discloses a microbial early warning and prediction system and method, the system comprises a data integration module, an operation modeling module, a model verification module and a total evaluation module, the data integration module associates public microbial information with customer information, and transmits the associated data to the operation modeling module; the operation modeling module operates and models the associated data, and transmits the result to the model verification module, the model verification module is used for verifying the accuracy and rationality of the individualized customized model, and transmitting the processed information to the total evaluation module, finally, the total evaluation module outputs a risk index, a risk description and a recommended improvement scheme according to the information of the operation modeling module. Due to the adoption of the above technology, the application comprehensively, systematically and multi-dimensionally analyzes various aspects of products and factories, achieves the purposes that prediction and recommended results are more accurate, application fields are more extensive, interpretation modes are more detailed and convenient, and the like.

The invention relates to a virtual power plant scheduling optimization method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the following steps: solving a day-ahead scheduling model according to a random variable generated by an uncertainty parameter model under the limitation of power constraint and energy balance constraint in combination with an operation cost model of a synchronous unit, an income model of a day-ahead market, an income model of a standby market and an operation model of a pumped storage unit, so as to obtain a current scheduling model; and determining the generated power, the adjustment reserve capacity and the expected profit of each component in the virtual power plant when the expected profit of the virtual power plant is maximum during scheduling. By adopting the method, the day-ahead market and the standby market are comprehensively considered, and collaborative scheduling optimization is realized. Therefore, the virtual power plant can reasonably arrange the operation strategy of each component in an uncertain environment so as to realize profit maximization. The method can adapt to a randomly changing market environment, and improves the robustness of an operation strategy and the operation reliability of the virtual power plant.

The invention discloses a robot unstacking and stacking method, and belongs to the technical field of industrial robots. Determining an unstacking area and a stacking area of the materials; according to the size parameters of the materials, an unstacking pile and a stacking pile of the materials are simulated and generated; according to the unstacking pile of the materials and the unstacking mode of the materials, an unstacking model of the materials is constructed in the unstacking area; the unstacking model is used for indicating an unstacking track and an unstacking sequence of the robot for unstacking the materials; according to the stacking pile of the materials, the unstacking mode of the materials and the stacking mode of the materials, a stacking model of the materials is constructed in the stacking area; and according to the unstacking model of the materials and the stacking model of the materials, the robot is controlled to unstack and stack the materials. A user only needs to input the material size, the pile type parameters and the operation mode, a complete operation model and a motion instruction can be automatically generated, efficient configuration of unstacking and stacking tasks is achieved, and the method is particularly suitable for industrial scenes such as flexible manufacturing, intelligent storage and logistics sorting which have high requirements for the automation degree.

The present invention discloses a method and system for adaptively adjusting the operating parameters of a vacuum coating machine, and relates to the technical field of operating parameter control of a vacuum coating machine. The system includes a data collection module, a control strategy generation module, a real-time monitoring and analysis module, and a data interaction module. The data collection module is used to collect the operating parameters of the vacuum coating machine, the control strategy generation module is used to produce optimal parameters, and the real-time monitoring and analysis module is used to monitor the production process. The method analyzes the main operating parameters of the vacuum coating machine through the control strategy generation module combined with the historical data collection unit, and determines the important parameters, constructs the operating model of the vacuum coating machine with the important parameters, sets the output conditions, obtains the optimal collection of important parameters, and outputs the specific indicators of each parameter to the data interaction module, and performs vacuum coating with these parameters, thereby ensuring the uniformity of vacuum coating parameters and the relatively stable coating quality of all products.

The present invention discloses a management method and system for a smart park based on digital twins, and relates to the field of digital modeling technology. When the system is running, a real-time park operation model is established using digital twin technology, thereby realizing comprehensive dynamic monitoring and optimization of the park's operating status. Through feature extraction and preprocessing, the system can accurately obtain resource usage and demand characteristics, and generate a more efficient resource scheduling index OSP. According to the optimized resource scheduling strategy, the production plan factor PPG is generated and tested, so as to adjust the production plan and achieve the balance and optimization of production tasks. The real-time monitoring and feedback module ensures real-time monitoring of the execution status, and generates a comprehensive optimization index COF through feedback vectors, further adjusting resource scheduling and production plans, effectively reducing operating costs, solving the large consumption of human resources, and reducing dependence on personnel mobility, significantly improving the operational flexibility and economic benefits of the park.

The invention relates to a virtual power plant scheduling strategy obtaining method and device and computer equipment. The method comprises the following steps: acquiring energy parameters, historical operation data and energy conversion efficiency of each component of a target virtual power plant; constructing an operation model of each component according to the energy parameters of each component of the target virtual power plant; sampling to generate a plurality of operation modes of the target virtual power plant according to the historical operation data; determining a risk index and a resource consumption index of the virtual power plant in each operation mode according to the operation data of each operation mode; determining a target operation model of the target virtual power plant based on the operation model of each component, each operation mode, the corresponding risk index and resource consumption index, and the energy conversion efficiency; and determining a scheduling strategy of each component of the target virtual power plant according to the target operation model of the target virtual power plant. By adopting the method, the operation efficiency and stability of the virtual power plant in a complex environment can be effectively improved.

This invention discloses a long-term optimized operation method for a regional integrated energy system considering the physical properties of hydrogen and multi-mode utilization. The steps are as follows: 1) Clustering source-load data using a clustering algorithm to obtain k typical days and the correspondence between typical days and actual operating days, k = h(r); 2) Establishing an equipment operation model based on equipment operating parameters; 3) Establishing a hydrogen utilization model under medium- and long-term multi-mode utilization; 4) Linearizing the equipment operation model; 5) Establishing a long-term optimized operation model of the Energy System (IES) considering LHS (Local Hybridization System); 8) Obtaining real-time operating data of the energy system and inputting it into the long-term optimized operation model of the IES considering LHS to obtain a long-term optimized operation scheme for the energy system. This invention can be widely applied to the evaluation of new energy AGC (Automatic Generation Control) power plants with different installed capacities, in different regions, and with different frequency regulation performance. It can provide a useful reference for the operation control of new energy participating in the secondary frequency regulation of the power system and the distribution of revenue in the auxiliary frequency regulation market.

The invention discloses a micro-grid-multi-EV aggregator mixed game optimization method considering low-carbon economic operation. The method comprises the following steps: constructing a single EV slow charge capacity boundary model considering user travel uncertainty; constructing a carbon transaction model considering a certificate-carbon fusion mechanism; an operation model is optimized based on a mixed game: an optimal energy selling price strategy of a regional user aggregator is determined through a master-slave game, and a micro-grid operator solves an equilibrium solution for minimizing the comprehensive cost of the two parties based on a pricing boundary; and secondly, each aggregator obtains electric energy transaction payment in the alliance through cooperative game solution based on the transaction power determined in the previous stage, allocates interests of each user according to asymmetric Nash negotiations with different user contribution degrees, and finally forms a multi-agent cooperation mechanism giving consideration to market benefits and environmental benefits.

This invention relates to the fields of software engineering and artificial intelligence, and particularly to an automated management system for the entire software development lifecycle, including a requirements analysis and architecture design module, a development and testing integration module, a deployment and operation monitoring module, and a visual management module. In the requirements phase, the invention performs semantic parsing of natural language requirements to generate structured documents, intelligently matches historical architecture patterns, and establishes a two-way traceability relationship. In the development and testing phase, it automatically generates code frameworks and test cases, performs incremental code review based on a defect prediction model, and automatically completes log cleaning, fault location, root cause analysis, and defect ticket closure when tests fail. In the deployment and operation phase, it intelligently generates canary release strategies, constructs a digital twin operation model, and enables multi-dimensional tracing and rapid handling of operational anomalies. This invention breaks down data barriers throughout the entire process with a unified digital mainline data layer, significantly improving software development and delivery efficiency and system operational stability.

The invention belongs to a data coding method, and provides a pumped storage production operation model data coding method and a related device aiming at the technical problems that in an operation control system of a pumped storage power station, an object in a production management system and model data in a production real-time system cannot be unified and are difficult to interact. Equipment related to the KKS model is classified through correlation analysis, and a double-layer structure of large-class dimensions and small-class dimensions is adopted. Evaluating the coincidence degree of the equipment types in the KKS model according to the equipment quantity of each equipment type in the large-class dimension and the small-class dimension, then establishing an equipment sub-book according to the coincidence degree, and further establishing a large class of the pumping and storage production operation unified model and a small class of the pumping and storage production operation unified model; on the basis, modeling is carried out on equipment types serving as objects, and then unified coding is carried out.

According to the cross-domain digital twin system based on the DIKWP model, the limitation that traditional twin only maps physical data is broken through, and a smart image covering five layers of data, information, knowledge, wisdom and intention is constructed. The system synchronizes sensing and business data of an entity in real time, dynamically calibrates an operation model, and embeds a decision strategy and a macroscopic target into a twinborn body to realize virtual-real consistency verification. The system has the functions of virtual-real comparison, trend prediction, abnormity early warning, decision evaluation and cross-department collaboration, interconnection and intercommunication of twins (such as traffic and energy) in different fields are achieved through a standard semantic interface, and system-level joint simulation is supported. Multiple technical achievements are integrated, a comprehensive management solution for complex systems such as smart cities and smart factories is formed, the cross-domain collaboration and intelligent decision-making ability is improved, and the method has wide application value and innovativeness.

The invention belongs to the technical field of new energy consumption cost calculation, and relates to a new energy consumption cost quantitative calculation method in a market environment. The method comprises the following steps: S1, extracting and constructing a new energy multi-dimensional characteristic vector; s2, establishing a mapping relation between the multi-dimensional feature vectors and the clearing boundaries of the power market, and calculating the demand up-regulation amount of each clearing boundary; s3, constructing an operation model corresponding to each time scale, and forming an operation constraint set; s4, identifying and classifying additional cost to form a consumption cost list; and S5, cost quantitative measurement and calculation are carried out, an operation simulation model and a long-term planning model are constructed and solved, the total system cost of the reference scene and the actual new energy characteristic scene is calculated and compared, and the difference value is the total consumption cost caused by the new energy characteristics. According to the method, the physical characteristics of new energy can be converted into quantifiable economic signals, so that more economical, efficient and safe novel power system transformation is supported and realized in multiple key fields such as power market construction, power grid planning and operation, energy policy making and the like.

This application provides a medium- to long-term power system planning method that considers the flexible production mode of nuclear power. The method includes constructing a system operation model with multiple power source categories based on resource data and power parameters, and constructing a capacity planning model with the objective of minimizing the annualized comprehensive cost of the system, combined with first-cost data. Constraints include nuclear power flexibility operation constraints, power supply and demand balance constraints, system reserve constraints, installed capacity constraints, cross-sectional constraints, carbon emission constraints, and energy storage operation constraints. Based on the nuclear power flexibility operation constraints and output data, the system operation model is used to perform production simulation to obtain load forecast data and second-cost data. Based on the load forecast data and second-cost data, the capacity planning model is solved to obtain the power system planning scheme. This method simultaneously considers economic efficiency, low carbon emissions, and flexible regulation capabilities, achieving multi-objective collaborative planning and providing scientific support for the optimization of the power source structure of new power systems.