104 results about "Peak load" patented technology

The invention discloses a charging pile self-adaptive charging planning method based on data mining, and relates to the technical field of self-adaptive charging, and the method comprises the steps: carrying out the real-time data collection and load prediction, dynamically adjusting a charging strategy, intelligently distributing charging resources in a peak load period, limiting high-power charging, and avoiding the overload of a power grid; in the idle load period, the idle capacity of the power grid is fully utilized, the power grid load is balanced, and the stability and safety of the power grid are guaranteed; through behavior data analysis and user portrait construction, users are divided into a high-priority type and a low-priority type, the charging rate and resource allocation are flexibly adjusted according to the requirements of different users, the high-priority users can obtain faster charging services, the low-priority users can enjoy more economical charging options, resource waste is avoided, and the user experience is improved. And meanwhile, the overall satisfaction degree of the user is improved.

The invention relates to the technical field of power supply, in particular to an emerging load power supply potential improvement method and system, and the method comprises the following steps: obtaining power demand data of a user in a plurality of time periods, calculating a load change rate in each time period, constructing a load behavior mode feature set, and obtaining load behavior mode feature data. According to the invention, by analyzing the user power demand data and calculating the real-time power demand comparison mapping information, the power demand can be effectively identified and adjusted, the coping capability and accuracy of power supply are improved, and by dynamically adjusting the discharge priority and time distribution of the energy storage unit, the power supply efficiency is improved. The use efficiency and response speed of energy storage equipment are improved, peak load shifting is facilitated, high efficiency and safety of power grid operation are kept, the overall performance and reliability of a power system are improved, the emergency response capacity of the power system to emergencies is enhanced, and the supply potential of power is optimized.

The invention discloses a multi-element energy storage joint scheduling method and equipment adaptive to peak load in an extreme scene, and relates to the field of energy storage scheduling. The method comprises the following steps: generating an extreme scene set by adopting a peak-valley difference characteristic-oriented clustering algorithm; generating an extreme scene uncertainty set by adopting a fast convex hull algorithm; adopting a multi-target generalized normal distribution optimization algorithm to generate a diversified energy storage power station aggregation model; based on the diversified energy storage power station aggregation model and the extreme scene uncertainty set, an intra-region wind-solar-fire storage robust optimization model is constructed, and the charging and discharging power of the diversified energy storage power station is solved by adopting a black-wing optimization algorithm; and according to the charging and discharging power of the diversified energy storage power station, a gradient directed evolution algorithm based on niche and a scheduling plan prediction model are adopted to generate an internal joint scheduling plan of the diversified energy storage power station. According to the invention, rapid decision-making of the internal joint scheduling plan of the diversified energy storage power station in the extreme scene can be realized, so that the stable supply capability of the power grid in the extreme scene is ensured.

The invention discloses a control method and device for a high and low voltage power distribution cabinet cluster, and relates to the technical field of power distribution cabinet control, and the method comprises the following specific steps: intelligent monitoring and evaluation: collecting the current and voltage parameters of each power distribution cabinet in the power distribution cabinet cluster in real time through a sensor, analyzing and calculating the load condition, by adopting an intelligent algorithm and a sensor technology, current, voltage and other key parameters of each power distribution cabinet in a high-low voltage power distribution cabinet cluster can be monitored in real time, the load condition is accurately evaluated based on the data, the power supply stability of important loads can be preferentially guaranteed in the peak load period, and the power supply reliability is improved. And meanwhile, appropriate power limitation is performed on non-critical loads, so that overload operation of equipment is avoided, idle power distribution cabinets can be identified and switched to an energy-saving mode in a low-ebb load period, and the energy utilization efficiency of the whole power distribution cabinet cluster is remarkably improved.

The invention discloses a bus peak load prediction method fusing transfer learning and peak load adaptive identification, and the method comprises the steps: constructing a dynamic weight-driven transfer learning framework, and achieving the precise migration of multi-source bus knowledge to a target bus; introducing a self-adaptive threshold mechanism based on load volatility and a peak value proportion, and dynamically identifying peak values and conventional load time periods; and designing a customized loss function for strengthening the peak error weight and using the customized loss function for fine adjustment of the model, thereby remarkably improving the load prediction precision of the bus in a key period. The target bus prediction precision is obviously improved through multi-source bus transfer learning; a self-adaptive threshold identification mechanism realizes dynamic judgment of a peak time period sensitive to load fluctuation, and the perception capability of the model to a key load time period is enhanced; through the synergistic effect of the technical means, the overall prediction accuracy is finally improved, meanwhile, the prediction precision and stability of the peak load are remarkably improved, and the actual requirements of a power grid for high precision and high robustness of load prediction are met.

The invention relates to the technical field of energy storage power supply control, in particular to an energy storage power supply control method and system and an energy storage power supply, and the method comprises the steps: calculating the complexity of all power grid load data in each peak load interval and the average level difference of each power grid load data compared with all other power grid load data; determining the response capability of the energy storage power supply; determining charging correlation factors of the energy storage power supply by analyzing correlation of variation trends between all power grid load data and all charging electric quantity data in each valley load interval and extreme distribution conditions of all power grid load data, constructing control hysteresis of the energy storage power supply in each time period in combination with the response capability, and determining the energy storage power supply according to the control hysteresis of the energy storage power supply in each time period. And controlling the energy storage power supply after the current moment. The invention aims to improve the absorption capability of the energy storage power supply for new energy power resources and the response timeliness of the energy storage power supply.

The invention relates to the field of electric power systems, in particular to an energy storage dynamic optimization scheduling method and system of an electric power system and a storage medium, and the method comprises the steps: S1, building an energy storage dynamic calculation model; s3, energy consumption models of a water chilling unit, an air source heat pump, a water pump and a cooling tower are established respectively, and instantaneous energy consumption under different working conditions is calculated by considering operation working conditions and environmental parameter correction; and S4, outputting the utilization rate, the maximum adjusting power, the average adjusting power, the maximum adjusting capacity proportion and the accumulative adjusting electric quantity proportion of the energy storage device, and carrying out quantitative evaluation. According to the method, supply and demand state changes such as valley load, flat load, peak load, peak load and the like of the power system can be identified in real time / quasi-real time, the time periods and power of energy storage and energy release are adjusted, dynamic power distribution of a host in each load time period is realized, and the overall energy efficiency level and operation stability of the system are improved.

The invention belongs to the technical field of new energy vehicle scheduling, and particularly relates to a double-layer optimization scheduling method for a pure electric heavy truck group to participate in peak clipping and valley filling of a power grid, which comprises the following steps of: processing running condition data of pure electric heavy trucks and road network data of a target area; basic energy consumption characteristics of the pure electric heavy truck are described; feature parameters are extracted and analyzed; fuzzy clustering is carried out on the driving condition information entropy of the pure electric heavy truck; path planning is carried out; selecting an optimal path to participate in response; sending a scheduling instruction to the pure electric heavy truck in the resource pool; establishing a pure electric heavy truck cluster charging load aggregation model for simulation; and making a scheduling plan. The method points out that the power and stress conditions of the pure electric heavy trucks are mainly influenced by the vehicle loads, a complete pure electric heavy truck energy consumption calculation process is formed according to influence factors, the scheduling method is realized based on the energy consumption constraint conditions influenced by the loads, and the scheduling scheme of the pure electric heavy truck group can better adapt to transportation tasks of different loads.

The invention discloses a distributed power supply energy storage configuration method and device. The method comprises the following steps: identifying peak load characteristics of a user side based on historical load data; analyzing output data of the distributed power supply according to the historical power generation data; establishing an energy storage optimization configuration model by taking maximization of user economic benefits as an optimization target; a double-layer optimization method is adopted, based on the energy storage optimization configuration model and peak load characteristics, a charging and discharging plan of the energy storage system is optimized at the upper layer, and a dynamic scheduling strategy is optimized at the lower layer; the real-time electricity price and the user load are predicted in a rolling mode, a dynamic scheduling strategy is combined, a charging and discharging plan is further corrected in the operation process of the energy storage system, the consumption proportion of renewable energy sources is optimized in combination with output data, and the energy storage system and the distributed power supply are made to operate cooperatively. According to the invention, optimal configuration and efficient scheduling can be carried out on the distributed power supply and the energy storage system at the user side.

The invention discloses a multi-time-scale energy storage configuration method and system for removing peak load, and relates to the technical field of power systems, and the configuration method comprises the following steps: generating a peak load fragment set based on an annual load curve; constructing a peak clipping load set under different durations based on the peak clipping load fragment set; calculating peak clipping load values under different durations, and sorting the peak clipping load values according to the durations from small to large to generate a peak clipping load distribution diagram; calculating the multi-time scale energy storage equivalent unit peak clipping cost under different durations; and according to the peak clipping load distribution diagram, determining power required by energy storage at different time scales. By constructing the peak clipping load distribution diagram, energy storage of different time scales is optimized and combined, the energy storage configuration cost can be reduced, calculation is simple, practicability is easy, and the peak clipping target is achieved with the lowest cost.

The invention discloses an urban rail energy consumption scheduling method based on artificial intelligence multi-objective optimization, and the method comprises the steps: effectively solving a problem of scheduling decision limitation caused by a single optimization objective in the prior art through comprehensive real-time data processing, multi-objective collaborative optimization and intelligent algorithm iteration solution; the optimal control of the overall operation energy consumption of the rail transit system is realized; the train operation energy efficiency, the regenerated energy utilization rate and the operation punctuality rate are effectively improved, meanwhile, the peak load of a traction power supply system is reduced, the overall energy utilization efficiency and the operation performance of rail transit are remarkably improved, and the dual purposes of energy conservation, consumption reduction and efficient operation of the system are achieved.

Operators typically utilize techno-economic means to set peak power demand in power infrastructure sites, such as power depots and microgrids. However, conventional means tend to either produce sub-optimal values of peak power demand or be too computationally expensive to be performed in a real-time or scalable manner. Accordingly, disclosed embodiments utilize a sliding time window to continuously or periodically determine peak power demand in past, current, and future portions of a current time period. These embodiments are able to determine an optimal peak power demand for the current time period, while remaining computationally feasible for real-time performance and being scalable with the complexity of optimization.

The invention discloses a peak load shifting potential evaluation system based on two-stage clustering, and the system is characterized in that a load data collection module is used for collecting historical power load data of a specified user, thereby obtaining a power load curve of the specified user during the implementation period of a peak load shifting project; the clustering analysis module is used for performing clustering analysis on the power load curve of the specified user during the implementation period of the peak load shifting project to obtain a user typical load curve set and a user power utilization regularity parameter; the power consumption mode determination mode is used for performing DTW and kmeans clustering analysis on the user typical load curve set to obtain a user power consumption mode; and the peak valley filling potential evaluation module is used for calculating a peak load shifting response factor according to the user power consumption mode parameter and the user power consumption regularity parameter, and calculating the peak load shifting potential of the user according to the peak load shifting response factor. According to the method, the peak load shifting potential evaluation of the user can be completed only according to the historical power consumption data.

The invention discloses a three-phase imbalance compensation method and system for a power distribution station area, and relates to the technical field of three-phase imbalance compensation, and the method comprises the steps: obtaining dynamic data in the power distribution station area, and carrying out the preprocessing of the dynamic data; constructing a digital twinborn model according to the dynamic data, and simulating operation scenes of a peak load scene and a valley load scene by changing a load condition and a power grid state; automatically adjusting a compensation strategy by using an intelligent optimization algorithm based on a result of the operation scene; and issuing the optimized compensation strategy to a compensation device in real time, and automatically adjusting the operation state by the compensation device according to the compensation strategy to realize real-time compensation of the three-phase imbalance. According to the method, the digital twinborn model is constructed, so that problems possibly occurring under different working conditions can be predicted in advance, and a more effective coping strategy is formulated; and the compensation strategy is automatically adjusted by applying an intelligent optimization algorithm, so that complex and changeable power grid conditions can be quickly adapted, and the compensation accuracy and timeliness are improved.

The invention discloses a peak-valley period demarcation method based on guiding charging load translation. The peak-valley period demarcation method based on guidance of charging load translation comprises the following steps: acquiring residential area data including private charging pile historical data and basic load data, and analyzing data features of the residential area data; establishing an upper-layer power grid total load model by taking peak load shifting as an objective function; establishing a lower-layer resident charging load model by taking the minimum charging cost as an objective function; according to the method, the charging load translation is guided, so that the defects in the aspect of charging period management in the prior art are overcome.

The invention relates to a high-temperature superconducting cable economical efficiency analysis method for urban power capacity increase, and belongs to the field of superconducting application. The method comprises the following steps: acquiring population data, electric vehicle permeability and charging pile configuration data of an urban area, and predicting the number of public charging piles and private charging piles newly added in the urban area; predicting a newly added load peak interval according to the predicted number of the public charging piles and the predicted number of the private charging piles in combination with an automobile charging coincidence rate; designing a superconductive low-voltage capacity increasing scheme based on the newly added peak load interval; acquiring system line loss data of the superconducting low-voltage capacity increasing scheme under different load conditions; and obtaining the annual operation and maintenance cost of the superconductive low-voltage capacity-increasing scheme based on the system line loss data, and carrying out economic analysis on the superconductive low-voltage capacity-increasing scheme based on the annual operation and maintenance cost. The objective of the invention is to solve the problems of lack of systematic economic evaluation means and difficulty in accurately judging the economic applicability of the high-temperature superconducting cable under the background of urban capacity increase in the prior art.

The invention discloses a new energy automobile charging pile layout optimization method based on big data analysis, and relates to the technical field of charging pile layout management. The method comprises the following steps of layout response judgment, layout test verification and layout decision training optimization. According to the method, the peak load response capability in the prediction process is quantitatively judged, so that the condition of charging pile overload or resource waste in the peak period can be effectively reduced, and the balance and adaptability of charging facility configuration are remarkably improved. Through layout decision response optimization, the response precision and timeliness of the model to peak load sudden change can be improved, and meanwhile, through obtaining a layout test verification result, the layout accuracy and the use efficiency of charging pile resources can be further improved. On the basis, charging pile planning layout decision training optimization is introduced, so that the charging pile layout decision is more accurate and higher in adaptability, and the actual effect and reliability of the layout decision are improved.

The present application provides an optimization scheduling method considering the participation of chicken house flexible resources in power distribution network peak load shifting, and relates to the cross technical field of facility agriculture and power grid optimization scheduling, which comprises the following steps: based on the industry load in the whole cycle, using the heat production equation and mechanism model equation of cage chicken house application scene, a typical cage chicken house daily constant temperature energy demand model is constructed; based on the constructed typical cage chicken house daily constant temperature energy demand model, the cage chicken house load is classified, and the classified load is superimposed to construct a load flexibility potential model; based on the constructed load flexibility potential model and the power grid peak load shifting model of adjustable load, by setting the objective function and constraint condition, and using the model optimization algorithm, the optimization scheduling scheme of power distribution network peak load shifting is obtained. The power grid peak load shifting model based on adjustable load constructed by the present application smooths the load curve of power grid, and realizes the economic operation of power grid.

The invention relates to a V2G cluster classification optimization scheduling method based on fuzzy control, and belongs to the field of electric vehicle cluster optimization scheduling. The method comprises the following steps: firstly, dividing electric vehicles into different V2G clusters according to state characteristics of the electric vehicles after network access; then, fuzzy distribution of input and output quantities is determined, a fuzzy rule is established, and a three-input-single-output charging and discharging power fuzzy controller is designed; and finally, making an optimal scheduling strategy according with the characteristics of the cluster according to the characteristics of the cluster, wherein the charging and discharging power of the cluster is controlled through fuzzy control in the strategy so as to achieve peak load shifting of the regional power grid load.

The present invention discloses a method for load forecasting and adjustment of a distribution network, which relates to the technical field of load forecasting of distribution networks. The method comprises the following steps: based on the management areas of different distribution networks in a city, the usage types of the distribution network are divided into residential, commercial, industrial, and mixed types, a city distribution network database is established, and distribution network archives are established based on the types; daily load data of different distribution networks in different historical months are collected, hourly predicted peak loads of different regions are calculated based on temperature intervals, and predicted surplus loads and predicted deficit loads are obtained in combination with the planned loads of different distribution networks, and surplus load time intervals and deficit load time intervals of different regions in different months are divided. The present invention predicts the load of the distribution network in combination with the characteristics of the management area of ​​the distribution network, and performs profit and loss management pairing on the distribution network in combination with the predicted load, thereby enhancing practicality and functionality.

The application discloses a thermal power unit transfer state peak load cruise control method based on an intelligent control algorithm and relates to the technical field of load coordination control. The thermal power unit transfer state peak load cruise control method based on the intelligent control algorithm determines the load demand change condition by classifying and processing data, realizes internal coordination of the thermal power unit based on the load demand change condition, realizes control operation on various factors in the boiler, analyzes the time for generating and implementing the instruction and the setting of the threshold value in the boiler, completes compensation and optimization of the coordination strategy through a compensation algorithm, completes the prediction operation when the load changes, analyzes the safety condition by using historical big data, obtains the desired prediction time, traces the corresponding load demand according to the prediction time, gives enough time to complete the coordination operation in the load supply process, and guarantees the operation safety of the thermal power unit.

The application discloses a kind of measuring crusher load benchmark value operation method and crusher control method, control system in crusher gathers the load of crusher once in a period of time, long enough time is continuously collected;The average of these load values is reference value A0, A0 is comprehensive load benchmark value;The average of load value greater than A0 is peak load benchmark value A1;The average of load value less than A0 is low load benchmark value A2;Control system automatically records above numerical value, and as the benchmark value of load related parameter;Control system statistics in a certain period of time, the number of load value greater than or equal to A1, and the average value N in the whole test data is calculated as peak quantity benchmark value;The number of load value less than or equal to A2 and the average value M in the whole test data are calculated as low load quantity benchmark value;These parameter values are automatically or manually changed after the next operation benchmark load test function;Make crusher can maintain optimal performance, maintain efficient work.

The present application relates to the technical field of elevator car, and discloses a kind of elevator car buffer system of sectional energy-absorbing structure, including shaft, shaft is provided with car, shaft and car are jointly provided with traction part, guide part and counterweight part, shaft and car are also jointly provided with buffer part, shaft and counterweight part are jointly provided with rail holding deceleration part.The elevator car buffer system of sectional energy-absorbing structure can effectively solve the problem that elevator buffer system exists in the process of car overspeed impact in the prior art, energy-absorbing response lag in initial buffering, impact process load distribution is uneven, and peak load at the end is seriously over-standard;And the end buffer in the elevator car buffer system does not have a matching rigid locking structure, rebound and secondary bounce will occur after the car is buffered, further exacerbating the problem of personnel injury and equipment damage.

The invention discloses a method and a system for configuring a differentiated reactive power compensation device accessed by a large number of cables. The method comprises the following steps of: determining basic information and incoming and outgoing cable parameters of a transformer substation; calculating a power factor during peak load and a power factor during valley load of the transformer substation, and analyzing a reactive power compensation device needing to be configured; the method comprises the following steps: calculating a capacitive reactive power compensation value and an inductive reactive power compensation value required by a transformer substation through iterative calculation, calculating compensated power factors during peak load and valley load, recording the corresponding capacitive reactive power compensation value and inductive reactive power compensation value before and after the compensated power factors reach the standard, and carrying out interpolation calculation on the values, and obtaining the reactive configuration capacity of the reactive compensation device needing to be configured. According to the technical scheme, the influence of access of a large number of cables on the reactive power of the power distribution network under the novel power system can be considered, so that the capacity of the reactive power compensation device is more reasonably configured, and stable and economical operation of the urban novel power system is supported.

A method for controlling a system (300) or a device (310) for identifying, checking, counting, accounting and sorting valuable items is proposed, which can automatically adapt to changing delivery and demand conditions. To this end, a controller (S) controls the transport paths and processing steps within the device (310) with the help of conveyors (140, 141, 142, 143, 325 and T) and corresponding diverters (245) and optimizes them in such a way that the valuable items are transported in a targeted manner to the processing level (P) required for processing the valuable items. n ). In addition, the controller (S) takes into account information about the components of the device (310) and the valuable items entering the warehouse and the valuable items to be shipped out of the warehouse to ensure optimal processing of the valuable items or to ship out the ordered valuable items as soon as possible. The device (310) can be configured so that it can handle a wide range of tasks and / or quickly handle peak loads by connecting its components in parallel. This also achieves reliable, safe and plannable operation.

The invention discloses a power system low-carbon operation optimization method and system. The method comprises the steps of S1, collecting multi-source operation data in a target power grid dispatching period; s2, an optimal scheduling model is established based on the multi-source operation data, a target function is the weighted sum of economic cost and carbon emission cost, and limiting conditions include unit operation limitation, system power balance limitation and safe operation limitation; s3, introducing a demand side response variable and constraint into the optimal scheduling model, establishing a mathematical model of an interruptible load and a peak-shifting load, and bringing the power adjustment amounts of the two types of loads into system global power balance calculation; and S4, solving the optimization scheduling model after introduction of the demand side response variables and constraints by using a hybrid intelligent optimization algorithm, and outputting a scheduling scheme including a unit output plan and an adjustable load response plan. Through the improved optimization scheduling model and algorithm, a scheduling scheme for reducing power grid carbon emission, peak load shifting and guaranteeing operation safety can be output.

The invention discloses a main transformer peak load prediction method based on electric power big data, and relates to the technical field of electric power systems. According to the method, the external influence data is subjected to combination and dimension reduction through rapid classification of the data by using a data classification result, so that the operand is greatly reduced, the prediction speed is improved, and the association relationship between the load data and the external influence data can be reserved to the greatest extent by the classification and dimension reduction method; therefore, the prediction accuracy is not influenced by using the external influence data after dimension reduction. According to the method, a large amount of historical data generated by operation and management of a power system is counted, analyzed, predicted and evaluated, information for scientific decision-making of a power enterprise is rapidly and accurately extracted from the historical data, a basis is provided for rapidly and accurately predicting the peak load change of a main transformer in the future, and the data mining processing method is established by establishing a data warehouse system. Accurate and reliable peak load prediction of the main transformer is realized, and the system is reasonably dispatched to operate safely and economically.

The invention provides a power distribution area distributed power supply autonomous consumption method based on source-load complementation, and the method comprises the following steps: S1, constructing a power distribution area model based on source-load complementation based on a power distribution area model based on source-load complementation; s2, establishing a day-ahead optimization regulation and control model according to the power distribution area model obtained in the step S1; and S3, solving the branch power flow by adopting the power distribution area branch power flow model obtained in the step S2 according to an optimization target in combination with a constraint condition. After the method provided by the invention is applied to optimized scheduling, the new energy consumption rate of a power distribution area is improved from 81.2% to 83.2%, and response scheduling of the electric vehicles in different time periods effectively promotes new energy consumption and peak load shifting, reduces the main network electricity purchasing and network loss cost, and realizes reduction of the total cost. According to the invention, the absorption capability of the distributed power supply is improved, and the operation economy of the power distribution network is improved.

The invention discloses a scheduling evaluation method and device for peak regulation participated by a vehicle cluster, and electronic equipment. The method comprises the steps of obtaining a peak clipping and valley filling time period when a vehicle cluster participates in peak shaving and a peak shaving index in the peak clipping and valley filling time period; in the peak load shifting period, generating a peak load regulation instruction based on the peak load regulation index, the power distribution network topological structure corresponding to the vehicle cluster and the schedulable potential value of the vehicle cluster in the charging station; in response to the peak regulation instruction, performing peak regulation scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster to obtain the initial charging power and the initial discharging power of the vehicle cluster; the initial charging power and the initial discharging power are evaluated, a dispatching evaluation result of the vehicle cluster is obtained, and the dispatching evaluation result is used for evaluating the stability degree of the power load of the vehicle cluster in the peak clipping and valley filling period. According to the invention, the technical problem that the scheduling condition that the vehicle cluster participates in peak regulation cannot be effectively evaluated is solved.

The invention relates to a demand response power consumer peak load management method and system and a medium, and the method comprises the steps: collecting hourly power consumption data of a consumer within a certain time range, and carrying out the preprocessing of the data, including abnormal value analysis and missing value filling; determining a target time period in which peak load optimization needs to be performed, performing descending order arrangement on the power data in the time period, and drawing a power duration curve in the time period; on the basis of a data smoothing method, a smoothed power duration curve is obtained, so that the overall change trend of the curve can be known; based on a marginal effect rule, determining an optimal hour number of user peak value optimization; and calculating a user peak reduction proportion corresponding to the optimal optimization hours, and estimating self electric charge which can be saved by the user through participating in demand optimization, thereby realizing economic potential evaluation. According to the method, the optimal peak clipping hours and the peak clipping amount of the user are accurately quantified, and an effective evaluation means is provided for screening demand optimization potential users of a power system.