21417results about "Ac network load balancing" patented technology

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

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.

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

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 relates to the technical field of power dispatching, in particular to a digital twin energy management method and system for source-network-load-storage cooperative dispatching, and the method comprises the steps: collecting source-network-load-storage multi-dimensional space-time operation data, and extracting space-time coupling features through a graph convolution-long and short-term memory network; establishing a simulation model of a digital twin environment, simulating an uncertain operation condition by using a Monte Carlo scene generator, and processing a power flow constraint by using a second-order cone relaxation technology; training an energy storage scheduling agent in a digital twin environment, and learning an energy storage charging and discharging strategy through a near-end strategy optimization algorithm; designing a source-network-load-storage hierarchical collaborative optimization framework, optimizing power output and load distribution by using an improved particle swarm optimization algorithm on the upper layer, and solving power flow distribution by using an alternating direction multiplier method on the lower layer; and establishing a self-adaptive feedback correction mechanism, and dynamically adjusting a cooperative scheduling strategy. According to the invention, intelligent collaborative scheduling of source network load storage is realized, and the operation efficiency and stability of a power system are improved.

The invention relates to a control method and system for a photovoltaic energy storage system, and the method comprises the steps: obtaining the real-time irradiance data of the photovoltaic energy storage system, carrying out the power generation prediction of an irradiance region, and obtaining a dynamic power generation prediction value; acquiring charge state parameters of a photovoltaic energy storage system and aggregated power load demand data of a target power utilization area, and performing energy storage scheduling analysis in combination with the dynamic power generation power prediction value to obtain a reference energy storage scheduling strategy; acquiring energy consumption equipment parameters of the target power utilization area, and performing equipment scheduling control with the reference energy storage scheduling strategy to obtain an equipment-level control instruction set; and performing grid-connected mode and off-grid mode collaborative analysis on the dynamic generation power prediction value, the reference energy storage scheduling strategy and the equipment-level control instruction set to obtain an energy storage global control strategy. According to the invention, multi-level collaborative optimization can be realized, and the economical efficiency and sustainability of the system are improved while stable power supply is guaranteed.

The invention discloses an optical storage and charging integrated micro-grid energy management system, which relates to the related technical field of micro-grids and comprises an electric energy collection layer, a digital twinborn layer, a collaborative optimization layer, an application execution layer, a photovoltaic power generation unit, an energy storage unit, a charging load unit and a distributed measurement and control terminal. The invention further discloses an energy management method of the photovoltaic, storage and charging integrated micro-grid. The energy management method comprises the steps of data acquisition, digital twin modeling, multi-target strategy generation and optimization, strategy evaluation and screening, strategy issuing and execution and closed-loop feedback and dynamic correction. According to the invention, through short-term prediction of a digital twinborn layer and real-time generation of a charging and discharging strategy by a collaborative optimization layer, fluctuating renewable energy sources are preferentially consumed, the light abandoning rate is reduced, energy storage charging is automatically triggered in an illumination peak period, and power overflow is avoided; based on the simulation result of the digital twinborn model, the power distribution of energy storage and load is dynamically adjusted, so that the photovoltaic utilization rate is improved, and the dependence on a traditional power grid is reduced.

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

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 provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention relates to an offshore energy platform coordinated scheduling method based on multi-energy complementation and hierarchical optimization, which combines multi-energy complementation characteristic modeling, multi-target opportunity constraint optimization and rolling optimization, and realizes offshore multi-energy coordinated scheduling by constructing a hierarchical decoupling optimization and control system. Based on prediction and historical data of multiple types of energy such as offshore wind power, photovoltaic energy and tidal energy, complementarity and flexibility of the energy are quantified, high-quality data support is provided for scheduling optimization, a day-ahead layered optimization model containing renewable energy priority consumption and flexible standby configuration is constructed, and a medium-and-long-term output strategy is formulated. Output of various energy sources is dynamically adjusted through a rolling optimization mechanism, and flexible response to renewable energy fluctuation is achieved. And finally, second-level frequency and voltage support is realized by using a virtual synchronous machine and droop control, and the self-adaptive capability of the system is enhanced. According to the invention, the cooperative regulation capability and operation stability of the offshore platform multi-energy system can be effectively improved, and the dependence on a traditional standby power supply is reduced.

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

The invention discloses a hierarchical collaborative management method and system for a virtual power plant based on multi-modal deep learning, and the method comprises the steps: constructing a four-dimensional data collection system, and achieving privacy enhancement preprocessing through federated learning and a differential privacy technology; a Bi-LSTM and a heterogeneous graph neural network are adopted to construct a three-mode deep fusion model, the weight is dynamically adjusted in combination with an environment-user dual-drive attention mechanism, and the load prediction precision and the space resource utilization rate are improved; a multi-target scheduling strategy is generated based on a five-dimensional target function and an improved DDPG algorithm, and physical feasibility is ensured through digital twinborn pre-verification; efficient execution and excitation transparency are realized through edge layer FPGA + NPU hardware acceleration and block chain evidence storage; and constructing a user participation ecology by using a natural language interaction strategy engine and a stepped incentive mechanism. The power grid economy, the equipment reliability and the user participation degree are remarkably improved, and intelligent upgrading of the virtual power plant is promoted.

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.