17153results about "Ac network voltage adjustment" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention discloses an intelligent power distribution layered self-healing fault isolation and power supply recovery method, which is suitable for realizing rapid fault isolation and power supply recovery of a non-fault area when a power distribution system has a fault. According to the method, a hierarchical control framework is adopted, and intelligent switch equipment on the lower layer is responsible for collecting the current and voltage states of a line in real time and immediately completing local identification and fault section removal after a preset fault signal is detected; the fault information is then uploaded to a power distribution automation master station on the upper layer, the master station analyzes a recoverable power supply area in combination with the current network topology and generates a network reconstruction scheme, and a non-fault area is switched to a standby power supply path by controlling an interconnection switch; the system supports the backtracking analysis of historical data such as waveform distortion and partial discharge signals before a fault, identifies a potential abnormal trend, optimizes an operation strategy, and achieves a self-adaptive early warning function. The method has the advantages of being rapid in response, clear in structure and high in autonomy, and the reliability and continuity of the power distribution system can be remarkably improved.

The present invention provides an energy storage synchronous coordinated management method and system based on virtual synchronization technology. The method comprises: selecting an appropriate energy storage device according to the requirements of a power grid and collecting related parameters; building a dynamic model of a virtual rotor, and setting rotational inertia of the rotor and torque parameters; building an adaptation layer; configuring a line monitoring apparatus, carrying out a safety operation when the fault occurs in the adaptation layer, or carrying out dynamic control and predictive planning in the absence of faults. The present invention utilizes intelligence to reduce reliance on experience, and enables dynamic optimization to improve the automation level of energy storage control.

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

The invention provides a dynamic balance system for wind, light, fire and nuclear storage integrated regulation and control of a power grid in a cold region, which relates to the technical field of power grid regulation and control and comprises an energy data acquisition and preprocessing module, a multi-energy coordinated regulation and control module, an energy storage system optimization management module, an energy scheduling optimization module, an intelligent decision and monitoring module and a system communication and safety module. A multi-level, multi-module and intelligent comprehensive framework is constructed, collaborative management and optimal scheduling of various energy sources are achieved, stable operation, efficient energy utilization and sustainable development of a cold region power grid are ensured, global optimization is achieved through deep reinforcement learning, local precise control is conducted through fuzzy logic, and the energy utilization efficiency is improved. The system can intelligently distribute the generated power of each energy source of wind, light, fire and nuclear storage according to the real-time state and environmental conditions of the cold region power grid, gives full play to the advantages of different energy sources, realizes the complementary utilization of the energy sources, realizes the collaborative optimization of multiple energy sources, and improves the utilization efficiency and stability of the energy sources.

The invention provides a new energy micro-grid intelligent control method and system, and the method comprises the following steps: obtaining voltage, current, power, frequency and temperature data streams in a micro-grid, achieving data synchronization through manifold mapping and geodesic distance calculation, and carrying out the data quality evaluation and abnormality correction based on geometric entropy and continuous coherence characteristics; mapping the processed data to a hyperbolic space to carry out feature extraction, carrying out multi-scale state representation by adopting hierarchical Riemann manifold decomposition, and establishing a differential topology dynamic model to carry out state prediction; a multi-objective function is constructed based on symplectic geometry, and a control strategy is generated through Morse-Smal complex optimization; and performing multi-level decomposition on a control strategy, and performing real-time execution monitoring and optimization adjustment by adopting an algebraic monitoring system. The invention provides a micro-grid intelligent control scheme with high precision and high reliability.

The invention relates to the field of source network load storage, in particular to a real-time coordination control method and system suitable for regional-level source network load storage. The technical problems that in the prior art, a regional-level source network load storage real-time coordination control method may cause missing and repetition of historical data and generation of abnormal values, resulting in influence on the accuracy of a prediction model, and may cause weak generalization ability and influence the prediction precision due to improper feature selection or unreasonable model selection are solved; according to the technical scheme, the real-time load-storage coordination control method and system suitable for the regional-level source network comprises the steps that predicted output power of new energy such as a photovoltaic power station and a wind power plant and predicted data of a power grid load are obtained in real time, and short-term and medium-term predicted output of the new energy is calculated through a prediction algorithm according to historical data and real-time data; according to the method, the data quality is improved through data arrangement, and the prediction precision and efficiency are improved by enhancing the generalization ability of the model and combining short-term and medium-term prediction.

The present application provides a multi-level coordinated voltage control method and system for a power distribution network based on three-tier priority objectives. The method comprises: acquiring operation parameters of a power distribution network; on the basis of the operation parameters of the power distribution network and a pre-constructed three-tier optimization objective planning model, using an affine theory, a duality theory, and power circle linearization and absolute value linearization methods to solve the three-tier optimization objective planning model to obtain maximization of an admissible net-load disturbance domain at each node, minimization of the total operation cost of the power distribution network and minimization of an expected voltage deviation; and performing coordinated control on various reactive devices of the power distribution network by means of the operation parameters of the power distribution network corresponding to the maximization of the admissible net-load disturbance domain at each node, the minimization of the total operation cost of the power distribution network and the minimization of the expected voltage deviation. The present application can more clearly characterize the uncertainty of distributed generation power output and the impact of the distributed generation power output on system reserve capacity, and mitigate the problems of ambiguous characterization of distributed generation power output characteristics, and multi-level voltage violation and voltage fluctuation of the power distribution network caused by large-scale distributed generation integration.

The invention discloses a wind-light-hydrogen multi-energy complementary system cooperative operation optimization and comprehensive benefit evaluation method, which relates to the technical field of electric energy storage, and comprises the following steps: constructing a hybrid power flow model containing electricity, hydrogen and heat coupling, taking whole life cycle cost and new energy consumption as double targets, introducing grid-connected and off-grid risk constraints, and optimizing equipment capacity configuration; a scheduling baseline and an intra-day dynamic correction strategy are formulated in the day-ahead stage, and hybrid energy storage cooperative control is combined to improve the response speed and flexibility of the system to wind and light fluctuation; a peak regulation and frequency modulation mode is switched according to the power grid state, a power response model is established, fluctuation is stabilized through hydrogen energy storage, the stability of the power grid is enhanced, and auxiliary service benefits are created; the performance of the system is evaluated from the three dimensions of efficiency, economy and interactivity, dynamic weight and Monte Carlo simulation optimization are utilized, system parameters are continuously optimized through a feedback mechanism, and the self-evolution ability is formed. The problem that system instability and economy are difficult to take into account due to wind and light fluctuation is solved.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention relates to the technical field of intelligent energy storage control, and discloses an intelligent energy storage control method, device and equipment based on bidirectional energy management. The method comprises the following steps: carrying out real-time acquisition and power grid state analysis on power grid parameters, energy storage module parameters, load requirements and photovoltaic power generation data to obtain power grid state evaluation indexes and a system operation mode division result; executing a multi-objective optimization algorithm to obtain an energy scheduling plan including a charging and discharging power instruction; converting the energy scheduling plan into charging and discharging current control parameters, and inputting the charging and discharging current control parameters into a control structure composed of a prediction control layer and a real-time response control layer in the three-phase hybrid inverter to obtain power output data; the power output data of the multiple three-phase hybrid inverters are uploaded to the intelligent management cloud platform, multi-machine parallel coordination control is executed, a distributed energy network management scheme is obtained, the dynamic characteristics of the system under different electric energy flow directions are predicted, and the stability and adaptability of the distributed energy network are enhanced.

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention provides a power distribution network symbiotic simulation system and method based on digital twinning. The system comprises a power distribution network digital twinning simulation platform, a cloud edge hierarchical deployment collaborative architecture and a power distribution network terminal. The digital twinborn simulation platform for the power distribution network comprises a data layer, a model layer, a service layer and an application layer which are covered in sequence by taking a cloud platform as a bottom, and the cloud edge hierarchical deployment collaborative architecture performs hierarchical deployment according to different service functions of the power distribution network. According to the invention, through the constructed full-attribute isomorphic power distribution network digital twinborn simulation platform and the cloud edge hierarchical deployment collaborative architecture, the fusion demand of multi-scene multi-service flow is fully considered, through the symbiotic simulation technology, dynamic feedback and collaboration between the simulation system and a real system are realized, and the highly integrated and collaborative simulation system has the advantages of high simulation efficiency and high reliability. Various changes in the power distribution network can be quickly responded, and a solid guarantee is provided for safe and efficient operation of the power distribution network.

The invention relates to the technical field of energy storage control, in particular to a grid-connected type light storage and charging energy storage control system which comprises a power bearing capacity monitoring module, a light storage power adjusting module, a grid-connected power feedback module, a power grid stability margin calculation module and an energy storage power dynamic control module. According to the method, the voltage and current data of the grid-connected point are acquired in real time, the power transmission capacity is calculated in combination with the rated voltage range and the safety power upper limit, the power adaptation precision of the grid-connected point is improved, the photovoltaic power generation grid-connected proportion is dynamically optimized on the basis of comparison between load growth rate calculation and the power adjustment range, and photovoltaic power output curve change rate analysis is combined. Power output fine control, calculation of the load change rate and combination of power grid power factor data are achieved, a charging and discharging mode adjustment strategy is optimized, the frequency change rate and fluctuation degree are monitored, the stability margin of the power grid is enhanced, frequency deviation of the power grid caused by load fluctuation is avoided, and the new energy consumption rate and the operation reliability of the power grid are improved.

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

The invention relates to the technical field of intelligent power grid regulation, in particular to a static concentration and dynamic distribution coordination control method for reactive power / voltage of a power distribution network. The method comprises the following steps: acquiring basic data of the power distribution network; performing multi-scale voltage stability domain analysis according to the basic data of the power distribution network to generate multi-scale voltage stability domain data; performing static centralized control strategy processing on the multi-scale voltage stability domain data to generate a static centralized control strategy; performing dynamic event trigger control processing according to the multi-scale voltage stability domain data to generate a dynamic distribution control strategy; and performing multi-region real-time cooperative control execution by using a static centralized control strategy and a dynamic distribution control strategy, and generating corrected cooperative control scheme data. By performing multi-scale voltage stability management and intelligent coordination control on the power distribution network, rapid dynamic response of reactive power and voltage is realized, and the voltage out-of-limit problem caused by active power fluctuation output is effectively suppressed.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention discloses an optical storage direct-flexible system power scheduling method based on market model prediction response, which comprises the following steps: determining the composition of an optical storage direct-flexible system, and collecting operation state data and external environment data; constructing a hybrid prediction model, and predicting the market price and the uncertainty interval of the market price in the scheduling period; establishing a power scheduling optimization model, and solving to obtain an optimal power scheduling strategy; according to the optimal power scheduling strategy, a control instruction is issued to each control unit of the optical storage direct flexible system, and cooperative adjustment is carried out through a bidirectional coupling feedback mechanism of voltage and power; the market price and the system operation state are monitored in real time, and when the market price deviation exceeds a price deviation threshold value, scheduling strategy online correction is triggered. According to the robust optimization architecture, traditional power scheduling is converted into a confrontation game structure between the system and the market, and the resistance of the system to extreme price situations is improved while economic benefits are guaranteed.

The invention discloses a source network load storage system cooperative regulation and control method and system, and the method comprises the steps: employing a high-precision sensor and multi-protocol communication to obtain system multi-dimensional data through global data collection and preprocessing, and carrying out the noise reduction; constructing a dynamic association model based on a graph neural network, and accurately capturing a system node relationship in combination with a multi-head attention mechanism and topological constraints; layered multi-objective decision, reinforcement learning real-time regulation and control, layered control architecture and closed-loop feedback correction are adopted, and economic optimization, safety guarantee and strategy iteration are considered. The system is composed of a global data sensing unit, a system dynamic modeling unit and the like, and all the units are in close cooperation. According to the method and the system, the defects of insufficient data processing, low model precision, single regulation and control and the like in the prior art are effectively overcome, the system fault prediction accuracy can be improved, the carbon emission is reduced, the power fluctuation coping capacity is enhanced, and the operation efficiency and the stability of the source network load storage system are remarkably improved.

The invention discloses a power distribution network intelligent optimization scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power distribution network operation optimization, and the method comprises the steps: recognizing the voltage fluctuation and stability problems of a power distribution network, and setting an optimization target; designing a multi-agent system, and defining agents to establish a load flow calculation model; optimizing the joint strategy by adopting a reinforcement learning method based on an anti-fact multi-agent strategy gradient; designing an optimization objective function to ensure physical constraint and equipment operation limitation; historical data are collected and preprocessed, and a reinforcement learning algorithm is utilized to train an intelligent agent; deploying the trained intelligent agent, collecting power grid data and adjusting a control strategy; and multi-time-scale optimization control is designed in combination with response characteristics of different devices. The method can effectively reduce voltage fluctuation, improve the power grid dispatching efficiency, enhance the flexibility and adaptability of the power grid, is suitable for intelligent power distribution network optimization dispatching of large-scale distributed power supplies, and improves the stability and the intelligent level of power grid operation.

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

The invention relates to the technical field of port intellectualization, and discloses a port flexible load multi-level interactive control method for ship-port network collaboration, which comprises the following steps: acquiring a basic data set, and processing based on a graph attention mechanism and a cross-layer attention mechanism to obtain a multi-layer graph feature tensor; probability distribution calculation and tensor decomposition processing are carried out on the multi-layer graph feature tensor and historical time sequence data, and a time sequence feature matrix is obtained; determining time window division points by adopting a dynamic programming algorithm, and processing based on a cross-scale attention mechanism to obtain a multi-scale dynamic feature set; combining the physical constraint data and the logic constraint data to construct a coupling feature matrix; based on the multi-time scale objective function and the system constraint data, an improved NSGA-III algorithm is adopted to obtain a cooperative control strategy set; and carrying out iterative optimization on the control strategy by adopting a reinforcement learning method to generate an optimal control strategy. Therefore, multi-level cooperative control of the port load is realized, and the dynamic response capability and the control performance of the system are improved.

The present disclosure describes a connector box, comprising a vehicle inlet port configured to receive a vehicle connector configured to connect to a direct current (DC) or alternating current (AC) vehicle charging station; a locking mechanism configured to secure the vehicle connector within the vehicle inlet port, wherein the locking mechanism is configured to be activated upon insertion of the vehicle connector into the vehicle inlet port and remain activated until deactivation by an authorized user; and an outlet port configured to supply electrical power received via the vehicle inlet port and to an apparatus, the apparatus configured to supply the electrical power from the outlet port to a vehicle connected to the apparatus in a vehicle charging session.

The invention provides a micro-grid energy management method and system based on deep reinforcement learning, and relates to the technical field of power grids, and the method comprises the steps: constructing a dual-time scale deep reinforcement learning model which comprises a day-ahead scheduling sub-network and a real-time scheduling sub-network; the day-ahead scheduling sub-network predicts micro-grid operation strategies at a plurality of time points in the future based on the long short-term memory network; the real-time scheduling sub-network is based on a depth deterministic strategy gradient algorithm, real-time state data and a day-ahead scheduling prediction result are fused to construct an evaluation function, an instant reward value is calculated, and renewable energy power generation, energy storage charging and discharging and an external power grid electricity purchasing and selling power adjustment instruction are optimized online. According to the invention, through dual-time-scale collaborative optimization, the energy management efficiency and economic benefits of the micro-grid are improved, and the operation stability of the micro-grid is enhanced.

The invention discloses a source-grid-load-storage integrated optimal scheduling method and system for a micro-grid, and relates to the technical field of smart grids and micro-grids, and the method comprises the steps: firstly collecting multi-source data in a specified micro-grid, and carrying out the optimization storage of the multi-source data in a time sequence database; constructing a time sequence prediction model, predicting generating capacity and load demand according to historical data, generating a preliminary scheduling strategy according to the generating capacity and load demand, realizing comprehensive cost minimization, analyzing the strategy, evaluating deviation and abnormity, and finally correcting the strategy according to an evaluation result; comprising an intelligent data aggregation module, a time sequence data prediction module, a preliminary scheduling generation module, a preliminary scheduling strategy analysis module and a real-time optimization feedback module, influences of various data sources are considered, the model can be adjusted in real time, variable power demands and power generation conditions in a micro-grid environment can be better coped with, and the real-time optimization feedback is realized. The accuracy of a prediction result is greatly improved, so that a powerful basis is provided for scheduling decision making, and risks possibly caused by prediction deviation are avoided.