562 results about "Power optimization" patented technology

An object tracking device, such as may be attached to a trailer to track movements of the trailer, may have various communication capabilities, such as cellular / LTE, Wi-Fi and GPS, which each have varying power requirements. A power optimization routine determines which communications should be performed to conserve battery while also providing frequent location updates. The routine determines if another plugged in tracking device is within low power Bluetooth (BLE) range and, if so, communications location via that device. If not, a Wi-Fi scan is performed and a lookup table is consulted for the corresponding location of any identified Wi-Fi access points. If location data cannot be found for an identified Wi-Fi access point, GPS location of the device is determined and the lookup table is updated to include the GPS location of the Wi-Fi access point.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention relates to a new energy charging pile dynamic scheduling method, system and device and a medium, and the method comprises the steps: collecting the real-time load data of a power grid, user charging request information, battery health state data and historical charging data, and carrying out the classification and aggregation of charging behavior characteristics, and generating behavior grouping labels; demand prediction and power safety boundary calculation are carried out based on a preset power prediction model in combination with the battery health state, and a power prediction result is formed; detecting power grid load conflicts and optimizing power distribution to generate a preliminary scheduling scheme; performing multi-target power optimization adjustment on the conflict points to generate a charging power scheme, and issuing and executing the charging power scheme; and finally, dynamically updating prediction model parameters according to charging pile feedback data. According to the method, the problems of neglect of battery health change, insufficient charging behavior distinguishing, response lag, weak collaborative optimization capability and the like in the prior art are solved, and collaborative optimization of power grid stability, equipment safety and charging efficiency is realized.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

The invention provides a circulation power optimization method and system for a three-phase dual-active bridge, and relates to the field of power electronics, the method comprises the following steps: determining an optimal backflow modulation strategy set corresponding to a hybrid modulation group, the hybrid modulation group comprising triangular wave modulation, trapezoidal modulation, synchronous modulation and extended phase shift modulation, the optimal backflow modulation strategy set corresponding to the hybrid modulation group comprises modulation modes and degree-of-freedom relationships corresponding to different transmission power per unit value ranges in different voltage conversion ratio ranges; determining a degree of freedom and power relationship set corresponding to the hybrid modulation group, wherein the relationship set comprises a relationship between the degree of freedom and power of different transmission power per unit values corresponding to each mode; the phase shift angle, the primary side duty ratio and the secondary side duty ratio are determined by combining the real-time voltage conversion ratio and the real-time virtual power per unit value, operation of the three-phase dual-active bridge is controlled, and the method has the advantages of reducing complexity and improving portability and efficiency.

The invention belongs to the technical field of charging pile thermoelectric control, and particularly relates to a high-power liquid cooling charging pile charging power optimization scheduling method and system. The method comprises the following steps: collecting operation state parameters of a plurality of liquid cooling charging piles; identifying a target pile body in a charging state or a standby state; calculating a theoretical energy supply upper limit based on an electrothermal model; combining the vehicle charging demand, the priority, the battery state and the power grid load to construct a power scheduling model and execute distribution; power adjustment and redistribution are carried out when cooling abnormity or connection faults are detected; and finally, a scheduling control instruction is generated and executed in real time. The system comprises an information acquisition module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. According to the invention, efficient, safe and dynamic power scheduling control of the high-power liquid cooling charging pile in a multi-device parallel connection scene is realized.

The invention discloses a heterogeneous intelligent computing power optimization management scheduling system for accelerating a large model reasoning task, and relates to the technical field of computing power optimization management scheduling. The video memory fragmentation problem in a long sequence scene is converted into a controllable block migration task, and the performance bottleneck of a traditional video memory exchange mechanism is broken through; based on an operator-level scheduling strategy of a hardware capability fingerprint database, position coding and other compute-intensive tasks are accurately matched with vector instruction set hardware, and resource mismatch loss caused by black-box scheduling is eliminated; an expert selection process is reconstructed by an integer routing and counting sorting algorithm, near-lossless reasoning is realized at a limited node of an instruction set, and the potential value of an old computing power pool is activated.

The invention discloses a hydroelectric generating set speed regulation control method considering excitation coordination. The method comprises the steps that 1, system state information is collected and preprocessed; step 2, on-line identification of excitation speed regulation coupling characteristics; 3, decomposing a multi-time-scale control target; step 4, designing a self-adaptive multivariable controller; 5, working condition self-adaptive parameter optimization; step 6, intelligently inhibiting the water attack effect; 7, multi-machine cooperative reactive power optimization control is carried out; 8, optimizing and executing a control strategy in real time; the technical problems that excitation speed regulation dynamic coupling, multi-time scale response mismatch, multi-machine cooperative control and the like in a hydroelectric generating set control system cannot be comprehensively solved in the prior art are solved, and the regulation response speed and the system operation safety are improved while the frequency and voltage stability is ensured.

The invention relates to the technical field of thermal management optimization, and discloses an active cooling and power optimization system of a plateau turbine generator, which comprises a multi-physics field modeling module, a reinforcement learning decision module, a strategy optimization training module and an edge execution control module, and is used for resolving a temperature field / flow field / stress field coupling state in real time, providing environment dynamic characterization, and optimizing the power of the plateau turbine generator. According to the method, multi-source sensing data is processed, a cooling liquid flow rate and cooling fin angle control instruction is generated, strategy iteration optimization is carried out in a digital twin environment, the physical rationality and energy efficiency optimality of a control algorithm are ensured, a lightweight strategy network is deployed, and sub-second-level real-time response and fault safety protection are achieved. Through intelligent control and multi-physical field cooperation, the temperature adjusting precision and the system response speed are improved, efficient power output of the turbine is kept under different altitudes and climate conditions through a self-adaptive strategy, and a reliable clean energy solution is provided for high-altitude areas in combination with multiple safety protection mechanisms.

The invention provides a computing power optimization method for green energy driving of a data center, and the method comprises the steps: obtaining real-time tidal energy supply data and load demand calculation data, analyzing the matching degree of periodic supply of tidal energy and load fluctuation demand calculation, and determining the peak dislocation time period of tidal energy supply and load demand calculation; evaluating an execution window of a high-energy-consumption calculation task of the data center according to a peak dislocation period of tidal energy supply and calculation load demand, and obtaining an available resource list in an energy sufficient period; if the migrated computing power resource allocation balance degree does not reach a preset balance threshold value, obtaining a current load of a server cluster of the data center, and determining an energy valley period activation condition; and analyzing the mitigation degree of resource mismatching from the available capacity index of the buffer energy, reallocating idle computing power resources according to the mitigation degree, and determining the supplement demand of the tidal energy shortage time period.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention provides a photovoltaic power station power optimization distribution method based on ultra-short-term prediction, and relates to the technical field of photovoltaic power station power management, and the method comprises the steps: 1, collecting the operation data and environment monitoring data of a photovoltaic array unit of a photovoltaic power station, and obtaining a unit power abnormality coefficient and a unit stability coefficient; 2, constructing a unit association network of the photovoltaic power station, monitoring and calculating the power interaction degree and the synchronous fluctuation rate between every two adjacent units in the association network, and obtaining a power association matrix and a fluctuation synchronization matrix; 3, according to the short-term power change rate, output compensation proportion prediction and capacity distribution adjustment are carried out, and an output compensation proportion and an adjustable capacity distribution scheme are obtained; 4, based on the power incidence matrix and the fluctuation synchronization matrix, obtaining a cooperative power anomaly coefficient and a system stability coefficient; and 5, generating a power optimization instruction set based on the unit power anomaly coefficient and the unit stability coefficient. And the output stability and the overall efficiency of the photovoltaic power station are improved.

The invention discloses a novel power system primary frequency modulation optimization method based on deep learning, and relates to the technical field of power optimization. The method comprises the following steps: collecting state data of a hydroelectric generating set running in a power grid, constructing a three-dimensional feature tensor, and extracting spatio-temporal features through a TCN-GRU hybrid network; constructing a deep reinforcement learning decision layer, and defining an action space and a reward function; constructing a PID neural network with a time-varying forgetting factor, and outputting a dynamic weight; establishing a dual-time scale updating mechanism; a safety verification module is arranged, and when the system frequency deviation value is larger than a threshold value, the traditional PID mode is switched. Characteristics are extracted from state data of operation of a hydroelectric generating set in a power grid, the time sequence dependency relation of the characteristics is processed through deep learning, the frequency change trend of the power grid is captured, the optimal control strategy is explored by applying the DRL technology, PID controller parameters are adjusted in a dynamic environment, and the optimal control strategy is obtained. And the primary frequency modulation response speed and the control precision of the hydroelectric generating set during power grid frequency fluctuation are improved through self-adaptive control.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention discloses a super capacitor cyclic charging and discharging energy storage control method and system, and belongs to the technical field of charging and discharging energy storage control, and the method comprises the following steps: setting a to-be-selected model list of a super capacitor, determining an optimal super capacitor configuration scheme in the to-be-selected model list according to a setting demand, and storing the optimal super capacitor configuration scheme in the to-be-selected model list; the charging and discharging constraint conditions of the energy storage equipment and the power transmission equipment are set through the optimal super capacitor configuration scheme; calculating a game equilibrium solution, and outputting a charging and discharging power optimization variable of the energy storage equipment and a power distribution optimization variable of the power transmission equipment; combining the detection data obtained by the sensor with the charging and discharging power optimization variable and the power distribution optimization variable to generate an energy storage equipment charging and discharging control instruction; and performing charging and discharging energy storage control on the energy storage equipment through the energy storage equipment charging and discharging control instruction. According to the method, the limitation of traditional single-index type selection is avoided, and the optimal super capacitor configuration scheme can meet the technical requirements and maximize the economic benefits at the same time.

The invention provides a hydraulic cooler heat dissipation power optimization method and system based on air flow, and relates to the technical field of hydraulic system heat dissipation, and the method comprises the steps: obtaining real-time operation parameters, and calculating the actual heat dissipation power; obtaining a target fan rotating speed of the maximum predicted heat dissipation power based on an improved particle swarm algorithm, and when a prediction error is greater than a threshold value, optimizing the model by adopting a deep reinforcement learning algorithm; and a fan rotating speed closed-loop control system is established based on sliding mode variable structure control, and the switching function gain coefficient is adaptively adjusted according to the environment temperature change rate, so that the fan rotating speed is dynamically adjusted. The cooling efficiency of the hydraulic cooler is improved, and the service life of equipment is prolonged.

The invention discloses a power conversion control method of a household energy storage system and a magnetic component, and the method comprises the steps: collecting an input voltage range, an output power demand and a frequency range according to the working characteristics of the system, selecting a nanocrystalline magnetic core material, and designing a multi-layer distributed winding structure to construct the magnetic component; acquiring operation monitoring data through a multi-dimensional data acquisition network, and generating a working condition description set through feature extraction and classification identification; a multi-target optimization model is constructed through bottleneck point identification and capacity estimation, and a power optimization distribution scheme is generated; then a control strategy selection space is constructed, an adaptive decision engine is operated, a PWM control strategy is selected, multiple PWM generation algorithms are realized, parameters are adjusted in real time, and a control signal is output; and finally, realizing system security monitoring and self-adaptive protection response through differential privacy protection processing and an anomaly detection model. According to the invention, the adaptability of the system in a wide voltage input range can be improved, and the loss of magnetic components under different load conditions is reduced.

The invention discloses a dynamic power optimization method and system based on bidirectional multi-agent reinforcement learning, and the method comprises the steps: collecting space-time joint channel data, an interference prediction result and neighbor base station feedback information, and constructing a base station multi-dimensional state space through a bidirectional real-time sensing mechanism; inputting the multi-dimensional state space of the base station into a bidirectional multi-agent reinforcement learning control module so as to carry out collaborative learning through a DEI-MADDPG algorithm, and constructing an output power adjustment strategy based on a collaborative Actor-Critic; generating a physical uplink shared channel control instruction based on a reinforcement learning strategy output result, and issuing the control instruction to realize dynamic allocation of base station transmitting power; and monitoring a network state and user feedback, executing strategy weight adjustment according to monitoring data, and returning to the step of constructing the base station multi-dimensional state space to realize base station transmission dynamic power optimization iteration closed-loop control. According to the invention, the transmitting power of the base station can be adaptively adjusted, and the whole SINR of the system can be improved while the same-frequency interference is suppressed.

The invention discloses a computing power allocation optimization method and system for multiple data processing tasks, and the method comprises the steps: only activating a minimum computing power equipment cluster which meets the current load upper limit when the load intensity is lower than a dynamic threshold value; when the load intensity exceeds a dynamic threshold value, collecting state data of each computing power node in real time, and generating a computing power feature vector and a task feature vector; training is carried out through a deep neural network model, a computing power optimization model is constructed, and computing power allocation is optimized; generating a plurality of groups of computing power allocation optimization schemes, extracting a test task data set to detect the actual optimization effect of each group of schemes, and obtaining the optimization scheme with the highest optimization effect; and when the newly added task or the node state change is too large, retraining the computing power optimization model. The method has the advantages that intelligent allocation of computing power resources is realized by monitoring task loads in real time and combining a deep learning optimization model, the resource utilization rate is remarkably improved, energy consumption is reduced, and the method has the advantages of high efficiency, flexibility and self-adaption.

The invention discloses an optimization system considering differentiated demand response of electric vehicles, the electric vehicles participating in demand response are divided into an agile contract-signing EV and a stable contract-signing EV, the agile contract-signing EV aims to maximize self-income, minimize mileage guarantee and load curve variance weighted difference, and based on an automatic demand response result of the agile contract-signing EV, the agile contract-signing EV performs automatic demand response of the agile contract-signing EV. And the stable contract signing EV performs output power optimization and aggregation by taking minimization of aggregation demand response cost as a target, performs autonomous demand response decision making based on deep reinforcement learning and autonomously participates in power grid demand response. The deep reinforcement learning converts a demand response scheduling problem into a Markov decision process, and the objective of optimizing an objective function of agile contract signing EV and an objective function of stable contract signing EV is achieved through a reward function. Compared with the prior art, the method has the advantages that the differentiated demand response of the electric vehicle is optimized through deep reinforcement learning, so that peak load shifting is effectively carried out on the power grid, and the demand response cost is reduced.

The invention provides a day-ahead reactive power optimization method based on a graph convolutional network and a conditional value-at-risk strategy. The method comprises the following steps: firstly, utilizing Monte Carlo simulation to generate a plurality of joint scenes of wind power and load uncertainty, extracting a typical operation scene through a K-means clustering method, and establishing a multi-target random reactive power optimization model taking active power loss and node voltage deviation as optimization targets; secondly, based on the optimization model, constructing a weighted adjacency matrix fused with power grid physical parameters, and enhancing the recognition capability of the graph convolutional network on the relationship between the nodes; then, a graph convolutional network model is trained to establish efficient mapping between a power system operation state and a reactive power regulation scheme, and rapid strategy generation is realized; and finally, performing cross-scene evaluation on the performance of each scene strategy and the voltage stability risk based on the conditional value-at-risk, and screening out a day-ahead optimization strategy with the best robustness. According to the method, the calculation efficiency and strategy robustness of day-ahead reactive power optimization are effectively improved, and the method is suitable for a power system with high-proportion new energy access.

The invention discloses a communication and inductance integration method for a communication waveform in a high mobile environment. The method comprises the following steps: constructing a UAV-ISAC system based on AFDM; constructing a model of a communication signal sent by the UAV to the base station and a model of an echo signal received by the UAV and perceived by a ground user; determining the signal-to-noise ratio of each time slot communication part link and the signal-to-noise ratio of a sensing part echo based on a communication signal model and an echo signal model, thereby determining the capacity of each time slot communication link and the sensing mutual information amount of each user, and determining the total sensing mutual information amount, the total communication capacity of the UAV in the whole flight time, and the total consumed energy; constructing a total optimization problem by using the total communication capacity and the total consumed energy, and decomposing the optimization problem into three sub-problems of user scheduling optimization, transmitting power optimization and UAV trajectory optimization; and by constructing different constraint conditions and solving the three sub-problems, an optimization result is obtained. According to the invention, the energy efficiency of the UAV-ISAC system based on AFDM can be maximized.

The invention relates to the technical field of indoor 5G network optimization, and discloses an indoor 5G network optimization system based on ray tracing, which is characterized in that a network signal state parameter set is dynamically acquired through a signal parameter acquisition module, a signal coverage risk is judged, and a collaborative analysis instruction is triggered to execute a ray tracing analysis module and an interference characteristic analysis module; and respectively acquiring a propagation stability index and an interference evaluation value. And the multi-source fusion judgment module receives the two values to carry out cooperative analysis on the network coverage adaptability, and generates antenna adjustment and power optimization signals. And the optimization parameter generation module is matched with the strategy to generate antenna angle adjustment and power correction parameters. The system further comprises a coverage coupling analysis module monitoring signal and building structure coordination and a dynamic feedback execution module to form a closed-loop control link, and intelligent optimization of an indoor 5G network is achieved.

The invention provides an optimization method and system suitable for scheduling of a source-network-load-storage integrated base. The method comprises the following steps: S1, updating and predicting information; s2, constructing an optimization model and embedding a collaborative strategy; s3, carrying out optimization solution; and S4, dispatching instruction issuing. The method focuses on the power optimization scheduling problem of a source network load storage integrated energy base, firstly, an optimization model with the operation cost minimization as the target is constructed, and the core of the optimization model is that a mixed operation mechanism and dynamic constraints of advanced adiabatic compressed air energy storage and batteries under the principle of main and auxiliary cooperation are depicted finely. Then, in order to effectively cope with uncertainty of new energy output, a model prediction control framework is introduced to carry out rolling optimization and closed-loop correction on a scheduling plan, and robustness of a strategy is improved. The strategy provided by the invention provides an effective solution for realizing economic and reliable operation of the integrated base at the high-proportion renewable energy source network load.

The invention relates to a station domain control method and system and a storage medium. The station domain control method comprises the steps of data acquisition and state monitoring, wherein the information of electric power flow, vehicle charging state, power grid load and energy storage state of a charging station is mainly acquired; load prediction and electricity price analysis: predicting an ultra-short-term load change trend by adopting a time sequence model LSTM to predict a future load, judging a time period in which the electricity price is located, and judging which time period the current time is in, namely, a peak time period, a flat time period and a valley time period; transformer load evaluation: making a control strategy by calculating available power headroom; power optimization distribution is carried out, and power dynamic adjustment is carried out according to different conditions; a charging pile strategy and an energy storage strategy are adjusted according to the load condition and the electricity price condition; executing a charging and discharging control strategy; the real-time dynamic adjustment refers to periodical adjustment of a charging strategy; according to the invention, electric energy transmission and state monitoring in the charging station can be effectively managed, and the real-time performance, reliability and safety of vehicle network interaction are improved.

The invention discloses a multi-mode local voltage control method and system based on a deep neural network, and relates to the technical field of power system operation optimization, and the method comprises the steps: constructing a multi-mode sample set comprising node voltage, photovoltaic output and load data, setting a voltage upper limit, a voltage lower limit and a hysteresis interval, and dividing operation modes; establishing a control mode selection unit by using a deep neural network, and establishing a reactive power output prediction model by using a convolutional neural network; and performing localized deployment based on the trained controller model, and performing node-level adaptive voltage control and real-time reactive power optimization according to the hysteresis interval and the residence time. According to the method, stable division and feature layering of voltage states are achieved, representativeness of model training samples and precision and stability of voltage mode recognition are improved, collaborative optimization of mode judgment and adjustment output is achieved, stable switching and dynamic optimization of the voltage states are achieved, and system immunity and stability of multi-node operation are improved.

The invention discloses a multi-area cooperative new energy consumption control optimization method and system, and relates to the technical field of new energy power generation, and the method comprises the steps: firstly calling historical operation data of a plurality of power supply areas, constructing a mutual assistance cooperation matrix through the source network load storage cooperation analysis, and building a mutual assistance power distribution topology according to the mutual assistance cooperation matrix; and each transformer area collects and uploads dynamic operation data to the absorption control middle transformer area, the middle transformer area carries out real-time optimization to obtain an absorption mutual transformer area combination, topology local area authority is reconstructed to obtain real-time flexible interconnection topology, and cross-transformer-area power flexible control is executed after the combination is sorted. The technical problems that in the prior art, intermittency and volatility of new energy are difficult to deal with, cross-zone-area resource optimal configuration cannot be achieved through a single-zone-area regulation and control mode, and consequently the new energy consumption efficiency is low are solved, and the technical effects of cross-zone-area power optimal allocation and improvement of the new energy consumption efficiency and the power grid operation stability and economical efficiency are achieved.

The invention discloses a rolling time domain reactive power optimization method and system containing a distributed photovoltaic power distribution network, and the method comprises the steps: firstly, employing a probabilistic prediction model to carry out the prediction of photovoltaic active power, and obtaining a future output expected value and an uncertainty interval; secondly, the prediction result is substituted into a multi-objective optimization problem with line loss, voltage deviation and reactive compensation as optimization objectives, and opportunity constraints are established by using the uncertainty interval so as to ensure the safety margin of the power grid voltage; and finally, performing rolling solution on the problem by adopting a multi-objective evolutionary algorithm guided by a power grid sensitivity index to obtain an optimal reactive compensation scheme. According to the method, through a closed-loop rolling mechanism of prediction, modeling and solving, prospective, collaborative and robust control of reactive power resources is realized, and the stability and economical efficiency of operation of the power distribution network under high-proportion photovoltaic access are improved.