107 results about "Power planning" patented technology

The invention discloses a new energy medium and long term stochastic programming method and system coupled with an electro-hydrogen water regulation power supply, and the method comprises the steps: building a power supply planning model with the minimum planning operation cost as an optimization target; bringing the new energy power supply and energy storage adjusting equipment operation model and the new energy consumption rate upper and lower limit constraint expression into a power supply planning model; in consideration of the randomness of fluctuation at two sides of a source and a load in power supply planning, based on the obtained power system operation scene set S and the power supply planning model, dividing into an investment decision-making layer and an operation simulation layer according to a time period influenced by a decision, and constructing a power supply planning two-stage stochastic optimization model based on a typical scene; and solving the power supply planning two-stage stochastic optimization model under a medium and long term planning framework, and determining the investment planning installed capacity of the adjusting power supply and the new energy in the planning level year. According to the invention, economy of medium and long term planning and power supply capacity configuration of new energy can be improved, and reasonable consumption and flexible adjustment of new energy electric power are promoted.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

The invention discloses a multi-scene coupled extreme weather adaptive power system planning method and system, belongs to the technical field of power system planning, and aims to solve the problem that traditional planning does not fully consider extreme weather influence and does not integrate multi-energy complementarity. Comprising the steps of collecting installed capacity, historical weather and power load data of a power grid unit; a conventional weather scene set is generated through clustering by adopting a kmeans algorithm, and an extreme weather scene set is generated in combination with types of extreme high temperature, cold waves and the like; constructing operation models and policy constraints of the thermal power unit, the new energy unit and the electrochemical energy storage unit; based on the planning-operation coupling framework, establishing a two-stage stochastic programming model with the goal of minimizing the total investment and operation cost; and integrating the multi-scene solving model, and outputting an adjustment power supply planning scheme. Through multi-scene coupling and two-stage optimization, planning and operation collaboration is realized, the economy, safety and adaptability of the power system in extreme weather are improved, and technical support is provided for low-carbon transformation.

The invention relates to a control method and system of a car home light storage and charging system and electronic equipment, and is applied to the technical field of vehicles, and the method comprises the steps that comprehensive energy data of the car home light storage and charging system are acquired, the car home light storage and charging system comprises multiple pieces of power equipment, and the power equipment comprises an inverter and a vehicle; according to the comprehensive energy data, determining an energy management strategy of the vehicle home light storage and charging system, wherein the energy management strategy is used for indicating the adjustment condition of the charging and discharging power of each device in the vehicle home light storage and charging system; obtaining an electric power planning strategy sent by the vehicle, wherein the electric power planning strategy is used for indicating the power utilization planning condition of each device in the vehicle home light storage and charging system; and based on the energy management strategy and the power planning strategy, controlling each power device in the car home light storage and charging system.

The invention relates to the technical field of new energy station transformation and multi-type power supply collaborative optimization, and discloses a new energy station transformation and multi-type power supply collaborative optimization method, system and device and a medium, and the method comprises the steps: obtaining a power system target parameter, and building and solving a first power supply planning model; preset disturbance is applied to the model, and frequency safety is checked. If not, adding system parameters and obtaining sensitivity cut constraints, and rebuilding and solving the model according to the system parameters and the sensitivity cut constraints. And after the conditions are met, outputting an optimization scheme. According to the method, the efficiency of a new energy station is improved, multi-power-supply collaborative optimization is realized, and the stability and reliability of a power system are enhanced. A power system is optimized in a targeted manner by accurately acquiring target parameters and establishing a first power supply planning model. And the model is solved and preset disturbance is configured, so that the frequency safety problem can be found and solved in time. In addition, by adding system parameters and obtaining sensitivity cut constraints, the scheme can be further refined and optimized, and accuracy and efficiency are improved.

The invention relates to an energy management method and system for a green electricity hydrogen production and hydrogenation all-in-one machine, and relates to the technical field of hydrogen energy application, the system comprises an upper layer control system and a communication interface module, collaborative optimization scheduling is executed in the day-ahead stage, the rolling stage and the real-time stage; the communication interface module supports IEC104, private UDP and other industrial protocols, and realizes real-time data interaction with a green electricity prediction system and a hydrogen production DCS system. The method comprises the following steps: making an electrolytic cell start-stop and power plan in a day-ahead stage; periodically correcting prediction and demand errors in a rolling stage; the power of the electrolytic cell is dynamically adjusted to fluctuate along with green electricity in the real-time stage. According to the invention, cross-section and multi-time scale coordination control is realized, and the green power consumption rate, the system operation economy and the hydrogen energy supply reliability are improved.

The invention discloses a chip design method, chip design equipment and a medium, and relates to the technical field of chips, during chip design, a target module in a chip is firstly determined, power supply planning of the target module is independently adjusted to avoid the influence of voltage drop on crosstalk when the wiring density is too large, crosstalk prevention is started in advance in the power supply planning stage, and the crosstalk is prevented from being damaged. After the layout and wiring design of the chip is carried out according to the power supply plan and the module layout, the crosstalk condition existing in the chip is directly analyzed, and if the crosstalk occurs in a target module, a mode of enlarging the layout space of the target module is adopted to cope with the crosstalk; if crosstalk occurs in other modules, a mode of adjusting electrical parameters of a signal line is adopted to cope with the crosstalk. Crosstalk prevention is carried out in the chip design stage, and different response modes are adopted according to different areas where crosstalk is located, so that the influence of crosstalk on normal working of the chip is avoided, and the accuracy and stability of the chip during working are improved.

The invention relates to the technical field of power planning, provides an AI-based virtual power plant energy supply and demand accurate matching method and system, and solves the problems of low accuracy of virtual power plant energy supply and demand matching and low overall operation efficiency. The method comprises the following steps: acquiring an output data sequence of power supply equipment in a virtual power plant and an electricity price data sequence of an electricity market; dynamically adjusting the initial charging and discharging power of the energy storage equipment in the virtual power plant to obtain target charging and discharging power; carrying out fusion optimization on the load prediction model of each energy participation node in the virtual power plant by using a federated learning mode, and generating a global demand adjustment plan; generating a power dispatching plan based on the output data sequence and the electricity price data sequence; and the power dispatching plan and the global demand adjustment plan cooperatively act on energy supply and demand matching of the virtual power plant, and AI-based accurate energy supply and demand matching of the virtual power plant is realized. According to the invention, the accuracy of energy supply and demand matching of the virtual power plant and the overall operation efficiency are improved.

The invention discloses a scene generation method and system for new energy extreme meteorological disturbance modeling, and relates to the technical field of power systems, and the method comprises the steps: recognizing a strong disturbance event under meteorological drive according to a constructed new energy effective output sample set, and constructing a generative adversarial neural network model embedded in a wind power photovoltaic physical boundary condition, acquiring a virtual disturbance section; and based on the virtual disturbance section, constructing a new energy annual extreme disturbance scene set by integrating a plurality of typical days embedded with disturbance, and applying the new energy annual extreme disturbance scene set to new energy extreme meteorological disturbance modeling and sample enhancement. The method has strong representation capability, good training stability and scene expansion capability, and can provide more representative and diversified input scenes for dispatching optimization and supporting power supply planning of the power system, so that the robustness and risk perception capability of the system for coping with extreme events are improved.

The invention belongs to the technical field of power planning, and particularly relates to an urban power utilization evaluation method based on power big data and a related device. The urban power utilization evaluation method comprises the following steps: collecting real-time power utilization behaviors of intelligent electric meter data, SCADA data and power grid GIS data to form multi-dimensional power utilization big data; constructing a knowledge graph based on the multi-dimensional power utilization big data and the topological structure association information; performing urban power consumption behavior analysis based on the knowledge graph to obtain power consumption behavior characteristic data; embedding the power consumption behavior characteristic data into a geographic space model by combining a geographic space technology, and mining regional power consumption distribution characteristics; based on regional power distribution characteristics, a spatial calculation algorithm is used, the power consumption population density is considered, and the urban power consumption layout is optimized. The problem that a traditional power system evaluation method cannot accurately evaluate the urban power demand is solved.

The invention relates to the technical field of power grids, in particular to a smart power grid environment adaptive data processing method and system. According to the method, through cooperative work of an environment sensing sensor, a high-definition camera and an unmanned aerial vehicle inspection system, omnibearing environment information including meteorological data, landform data, vegetation coverage data and human activity data is obtained; a distributed storage architecture and a parallel processing technology are adopted to efficiently pre-process the environmental data; constructing a smart grid environment adaptive analysis model covering power planning decision support, power operation and maintenance decision support and power dispatching decision support; an analysis result is visually displayed through a multi-level visual interface based on the GIS technology, and environmental adaptability optimization of active defense, automatic recovery and optimal adaptation is achieved. The problems of uneven data quality, low processing efficiency, insufficient analysis capability and the like of a traditional power grid are solved.

The invention belongs to the crossing field of power system dispatching automation and artificial intelligence, and particularly relates to a parameter dynamic generation technology based on cooperation of a large model and a mechanism model. Comprising the following steps: S1, receiving natural language instruction input of a dispatcher; s2, intention recognition and instruction analysis are carried out through the power planning model, and key scheduling parameters are extracted; s3, the analysis result is converted into mathematical parameters which can be executed by the mechanism model; S4, the mechanism model carries out multi-objective optimization calculation based on the input parameters, and a preliminary scheduling scheme is generated; s5, performing semantic verification on the preliminary scheme through the large model; and S6, dynamically adjusting the parameter weight according to the verification result, and iteratively optimizing the scheduling scheme. Through deep fusion of natural language processing and a power system physical mechanism, full-process intelligent conversion and closed-loop optimization from a dispatcher instruction to a mathematical model parameter are realized, and limitation of a traditional static rule is broken through.

The invention discloses a new energy grid-connected system voltage sensitivity index evaluation method based on an entropy weight method. According to the method, firstly, construction of a new energy grid-connected system voltage sensitivity evaluation index system is researched; and constructing a new energy grid-connected system voltage sensitivity evaluation index system by integrating a reactive power response coefficient reflecting reactive power disturbance and a voltage control factor reflecting voltage supporting capability. Secondly, a new energy base simulation model is constructed, power flow simulation is carried out, and index values of reactive response coefficients and voltage control factors of new energy station nodes are obtained through calculation; furthermore, how to apply the entropy weight method to new energy grid-connected system voltage sensitivity index evaluation is researched, a scientific evaluation result is obtained, a voltage weak point is determined, and a theoretical basis is provided for reactive power planning.

The invention discloses an EMIR verification method and system, a computer and a storage medium. The method comprises the following steps: appointing a time sequence library file directory, an RC file path and a constraint file path when an automatic script runs; traversing a time sequence library directory through an automatic script, analyzing time sequence library files, and extracting a PVT condition and an RC model corresponding to each file; automatically classifying and mapping the extracted PVT condition and RC model according to a preset classification rule to generate a multi-process corner combination; automatically generating a multi-mode multi-corner-point MMMC configuration file according to the multi-process corner point combination, and outputting a traceable mapping list and an association table; in a chip physical implementation process, dual-stage EMIR verification is executed in a power supply planning stage and a verification stage. According to the method, the MMMC configuration file matched with the angular point is automatically analyzed based on the time sequence library, and staged EMIR verification is combined in the physical implementation process, so that the overall design efficiency and reliability are improved.

The invention provides a large model application system for energy power planning and intelligent optimization. The system comprises a data acquisition end and a model application end, the data acquisition end is used for acquiring, integrating and storing multi-dimensional electric power information of an electric power system; the model application end is used for intelligently planning and optimizing the power transmission of the power system based on the data acquired by the data acquisition end, so as to ensure the efficient utilization of power resources and the stability of the system; through multi-dimensional data integration and intelligent optimization, efficient distribution and stability guarantee of electric power resources are realized, and the flexibility and response capability of an electric power system are improved.

The application discloses a kind of grid-connected detection method, equipment, medium and product of AC-DC distribution system, the method includes: establishing the power flow model of AC-DC distribution system before distributed power grid connection, and according to power flow model obtains time series power flow result, the reliability calculation of AC-DC distribution system is carried out, obtains reliability calculation index, according to time series power flow result and reliability calculation index, the comprehensive performance index of AC-DC distribution system is constructed, according to comprehensive performance index, the operation state information of AC-DC distribution system is generated for distributed power grid connection.According to operation state information, the weak link that AC-DC distribution system needs to be reformed can be determined, and technical support and guidance can be provided for distributed power grid connection strategy, which is conducive to promoting the safe and reliable operation of AC-DC distribution system.Through optimization scheduling and power supply planning, the operation efficiency of AC-DC distribution system can be improved, the operation cost is reduced, and the accuracy of grid-connected detection of AC-DC distribution system is improved.

This invention discloses a method, apparatus, equipment, medium, and product for power configuration in clean energy bases, belonging to the field of power configuration technology. The method for power configuration in clean energy bases includes the following steps: obtaining demand data for various power configurations; constructing a power configuration model for the clean energy base based on the demand data; and generating a power configuration scheme for the clean energy base using the power configuration model. This invention constructs a power configuration model applicable to integrated wind, solar, coal gas, and hydrogen storage systems. It eliminates repetitive manual work, significantly shortens the design workflow, improves power planning and design efficiency, and eliminates reliance on manual experience, enhancing the scientific rigor and standardization of various power configuration schemes, improving the quality of power planning and design, and reducing power planning and design costs, thereby promoting the development of clean energy bases.

The invention discloses a conventional unit installed capacity estimation method and system based on a load characteristic index, and belongs to the technical field of power system planning. The method comprises the following steps: acquiring historical load information, and calculating daily load and annual load characteristic indexes; deriving and planning annual total power consumption based on indexes, and calculating the minimum installed capacity of a conventional unit through annual and typical daily power balance; and energy storage peak regulation parameters are further calculated, and climbing verification and power receiving correction are carried out. The system comprises a data module, an index module, a capacity module and a correction module. The time sequence characteristics are quantified through the key load indexes, the complex power source planning process is simplified, rapid and accurate estimation of the installed capacity is achieved, the method is suitable for a new energy high-permeability system, and the planning efficiency and economical efficiency are improved.

The invention discloses a transformer area three-phase balance and regional load balance method, device, equipment and medium, and belongs to the field of power distribution network power planning, and the method comprises the steps: screening a first sub-region with unbalanced load from each sub-region according to the total energy consumption of each sub-region in a to-be-planned region and the total number of users; obtaining and preprocessing three-phase current time sequence data of each first device in the first sub-region; the energy consumption of the first equipment is greater than preset energy consumption; according to the preprocessed three-phase current time sequence data, screening out a second device with unbalanced three phases from the first devices; constructing a multi-objective optimization mathematical model by taking the three-phase current balance of each second device and the load balance of the to-be-planned area as optimization objectives; and according to a solving result of the multi-objective optimization mathematical model, redistributing a connection phase of each second device and a sub-region to which the second device belongs. According to the invention, the problem that the three-phase balance and regional load balance of the low-voltage transformer area of the power distribution network cannot be optimized in time in a dynamic environment can be solved.

The present invention relates to the technical field of power grid investment optimization, and specifically discloses a power grid investment optimization method based on big data analysis, the method comprising: monitoring meteorological data of a power planning area, processing to obtain a time series function of power generation of new energy sites in the power planning area, predicting the demand load based on historical load data of the power planning area, and comprehensively analyzing to obtain the demand construction scale data of the energy storage system in the power planning area; obtaining new energy vehicle usage data in the power planning area, and comprehensively analyzing to obtain virtual energy storage compensation parameters in the power planning area; and optimizing the demand construction scale data of the energy storage system in the power planning area based on the virtual energy storage compensation parameters in the power planning area. The present invention comprehensively considers the charging demand and discharge capacity of electric vehicles, determines the optimal construction scale of the energy storage system, and provides a more scientific basis for power grid investment optimization.

The invention belongs to the technical field of AC / DC hybrid power grid reactive power planning, and particularly relates to a target scene-oriented AC / DC hybrid power grid reactive power planning parameter optimization method, which comprises the steps of S1, establishing a reactive power planning model considering reactive power equipment distribution, capacity and internal parameters, and introducing a reduced-order mapping matrix to pre-reduce variable dimensions; s2, performing simplified linearization processing on the transient stability constraint optimal power flow problem of the power system, and performing analysis and processing to obtain the linearization stability constraint of the reactive power planning model; s3, removing the corresponding minimum probability distance scene from the set until the representative scene is equal to a preset value; s4, integrating the linearization stability constraint of the reactive power planning model and the representative scene set into the reactive power planning model to obtain an optimal power system reactive power planning model; and S5, performing dynamic reactive power planning by using the optimal power system reactive power planning model. According to the method, the planning efficiency can be effectively improved on the premise of ensuring the voltage qualification rate.

The invention discloses a power distribution network reactive power planning method based on an improved point estimation method and chance constraint, and the method comprises the steps: S1, carrying out the mixing moment calculation and sample point selection of an uncertainty parameter through employing the improved point estimation method, and obtaining an estimation value of the uncertainty parameter; the uncertainty parameters comprise distributed power supply output and load; s2, constructing a power distribution network reactive power planning model based on opportunity constraint, taking the minimum comprehensive operation cost of the power distribution network as a target function, and determining various constraint conditions; the constraint conditions comprise a power flow equation equality constraint of the power distribution network, an inequality constraint of a node voltage amplitude, branch current and reactive compensation equipment capacity, and a chance constraint of the node voltage amplitude and branch power; and S3, substituting the estimated values of the uncertainty parameters into the reactive power planning model, and solving the reactive power planning model by adopting a genetic algorithm combined with quantum behaviors. According to the method, the defects of a traditional point estimation method and deterministic constraints are overcome, and a better reactive power planning scheme is obtained.

The application provides a multi-objective power supply planning method based on Pareto front shape optimization and a related device, including obtaining a multi-objective power supply planning model of a project to be planned, and extracting investment decision variables and operation variables from the multi-objective power supply planning model; continuously optimizing solutions composed of the investment decision variables and the operation variables by using a genetic algorithm until the optimization is stopped after a stop condition is met, and a solution set is obtained; selecting a compromise solution meeting a requirement based on a Pareto front shape of the solution set, so as to determine a final multi-objective power supply planning scheme. The application optimizes the investment decision variables and the operation variables by using the genetic algorithm, minimizes conflict relationships between different objectives, selects a balanced compromise solution, and finally determines the multi-objective power supply planning scheme. The method not only considers uniformity and convergence of the Pareto front, but also particularly focuses on influences of the power supply planning on conflict relationships of operation objectives, and improves accuracy and reliability of decision-making.

The invention provides an electric power planning method for renewable green new energy development, and belongs to the technical field of energy planning, and the method comprises the steps: calibrating a plurality of supply points in a region according to the position of each electric device in the current region and the type of an energy demand in combination with a change plan; performing preliminary modeling on each building in the area, and inferring an energy transformation scheme by combining the energy demand quantity and the energy demand type designed by each supply point; transmitting energy generated by the new energy system to a plurality of corresponding electric devices for consumption; and the new energy system is integrated into the current actual construction scheme and the building construction scheme to be carried out in the future, and finally energy supply transformation of the buildings in the area is completed. According to the electric power planning method for renewable green new energy development, the proportion of new energy supply of the whole area can be increased, efficient nearby consumption of energy is achieved, dependence on traditional energy is reduced, and a limited new energy system structure is optimized.

The application provides a multi-time scale power supply guarantee analysis method and system, which comprises the following steps: collecting load parameters in a system to be studied; performing load characteristic analysis and new energy characteristic analysis based on the load parameters to obtain preliminary typical day simulation analysis results; performing typical day simulation calculation based on the preliminary typical day simulation analysis results to obtain an intraday power supply guarantee result, and ending the power supply guarantee analysis if the system has no power shortage; if there is a power shortage, performing the next step of analysis; if there is a power shortage, performing monthly power balance calculation to measure seasonal power shortage, and formulating a seasonal power supply guarantee scheme; after the seasonal power supply guarantee scheme is adopted to ensure seasonal power balance, performing continuous multi-day power balance calculation to measure continuous multi-day peak shaving demand, formulating a continuous multi-day power supply guarantee scheme according to the size of the extreme weather power shortage, and performing typical day simulation analysis again if there is no power shortage. The method can quantitatively analyze the peak demand of different time scales in the system during the power supply guarantee period, and provide support for power source planning.

This invention belongs to the field of power planning and new energy power generation. It discloses a method, system, equipment, and medium for configuring the capacity of an amphibious power plant based on monthly balance. Based on the load forecast of the amphibious power plant and the predicted new energy power generation during the hydropower peak and off-peak seasons, a monthly power balance analysis of the amphibious power plant is performed to obtain the power deficit for each power-deficient month and the power balance for each power-excessive month. Based on the power deficit for each power-deficient month, the required coal-fired power generation capacity to ensure power supply during each power-deficient month is determined, and the maximum value is selected to obtain the coal-fired power generation capacity of the amphibious power plant. Based on the power balance for each power-excessive month, the required energy storage capacity to ensure the complete consumption of abandoned new energy power during each power-excessive month is determined, and the maximum value is selected to obtain the energy storage capacity of the amphibious power plant. This achieves a reasonable determination of the coal-fired power generation capacity and the energy storage capacity of the amphibious power plant.

The invention relates to the technical field of power supply planning of an electric power system, and discloses a power supply planning method, system and device and a medium, and the method comprises the steps: obtaining key parameters of the electric power system, and generating a plurality of source-load random scenes; based on these scenarios, an uncertain set of component faults is constructed, thereby establishing power disturbance and system frequency response equations, including frequency balance constraints. And constructing a power supply planning model including an objective function and constraint conditions, and solving the model to optimize power supply configuration. According to the method, the reliability, the stability and the adaptability of the power system are improved by considering the uncertainty and the frequency stability. Stable operation of the system under various conditions is ensured, the optimal power supply configuration is rapidly determined through the planning model, the planning efficiency is improved, resource waste is reduced, and sustainable development is promoted.

This invention belongs to the field of power planning and design technology, and discloses a method and system for simulating wind farm output. The method first calculates the theoretical output, stochastic reduction factor, low temperature reduction factor, and maintenance reduction factor based on the target basic data of the project location, and calculates the hourly comprehensive reduction factor. Then, the actual output is calculated based on the theoretical output and the hourly comprehensive reduction factor. In this way, the impact of uncertainties such as temperature and maintenance on the output during the actual operation of the wind farm is taken into account. Based on mesoscale reanalysis data or measured wind speed data, it can quickly and accurately simulate the actual output data of the wind farm without the need for a complex modeling process, which can meet the needs of wind power project output prediction.

The present application relates to the technical field of data processing, and especially relates to a power planning optimization method based on artificial intelligence, which comprises the following steps: collecting data; determining abnormal events; predicting parameters; determining candidate shutdown equipment; determining high-risk equipment; determining target shutdown equipment; generating a power planning scheme; and adjusting thresholds. The present application collects multi-dimensional data in real time, determines abnormal events according to total power consumption, predicts load loss and residual time in combination with a preset model, determines high-risk equipment according to residual time, heat consumption rate, vibration frequency and rotor speed, determines target shutdown equipment according to active power, climbing rate and load loss of high-risk equipment, and adjusts thresholds dynamically according to total power consumption and rotor speed based on the power planning scheme, thereby effectively solving the problems of low precision of shutdown decision and response lag caused by the complexity of the operation background of the power system and excessive dependence on models.

The application discloses a novel power system multi-dimensional supporting demand index evaluation method and system, belongs to the field of power system demand estimation, and comprises the following steps: based on system load data and new energy output data, analyzing power system net load data conditions; based on the power system net load data conditions, screening extreme weather new energy continuous low output data according to the new energy output data, determining an extreme weather time segment, and acquiring extreme weather period net load data; and based on the extreme weather period net load data and total time sequence net load data, determining a supporting demand index in the time sequence, and supporting new power system power sources for planning. The application more comprehensively constructs a multi-dimensional supporting index system of supporting power sources, and provides a reference basis for new power system supporting power source planning.